# Muse Spark Open vs DeepSeek V4.1 vs Beam: 1M Context [2026] > Source: > Published: 2026-11-06 05:12:59+00:00 DeepSeek shipped [V4.1 Flash](https://deepseek.com/en/news/deepseek-v4-1-flash/), and later two U.S. labs answered with open-weight releases of their own: Meta’s Muse Spark Open on October 15 and Reflection’s Beam 1.0 weights on October 16. All three list a context window of one million tokens. They split hard on size, modalities, price and who gets to run them. That makes this the most useful three-way fight in [open weight models](https://shattered.io/open-weight-ai-models-side-by-side-the-spec-sheet/) right now. DeepSeek V4.1 Flash is the cheap, multimodal API workhorse that also happens to be a very large mixture-of-experts model. Muse Spark Open is the heavyweight with audio and video input. Beam 1.0 is the Apache 2.0 option aimed squarely at coding agents. One caution before the tables. Our fact check found no independent benchmark that ranks all three against each other, so this comparison leans on confirmed specs, licenses, prices and deployment evidence. Where a number is vendor-reported or comes from a single source, the text says so. Don't miss new tech stories on Google Add Shattered once in the Google app and our stories show up in your news suggestions. [Add now](https://www.google.com/preferences/source?hl=en&q=https%3A%2F%2Fshattered.io%2F) ## Muse Spark Open vs DeepSeek V4.1 vs Beam: The Short Answer Pick DeepSeek V4.1 Flash if your priority is price per token and you want a hosted API with native multimodal input. Its off-peak output rate is $0.60 per million tokens, and DeepSeek says off-peak rates are 50% of peak rates. No other model in this trio has a first-party price list that low. Pick Muse Spark Open if you need a model that reads text, images, video and audio and you have serious hardware or a hosted provider. Meta describes it as its “most capable US open-weight model to date,” and it is the largest of the three at 816 billion total parameters with 42 billion active per token. Pick Beam 1.0 if licensing clarity and coding agents matter most. It ships under Apache 2.0, carries 501 billion total and 23 billion active parameters, and Reflection positions it for software engineering, tool use and long multi-step workflows. Hosted pricing on OpenRouter starts at $0.25 per million input tokens and $1 per million output tokens. The cleanest way to read the three is by job. DeepSeek is the budget generalist, Meta is the multimodal flagship, and Reflection is the permissively licensed coder. None of them is a drop-in replacement for the others, and the migration section below shows where the friction sits. ### Quick scorecard | Question | DeepSeek V4.1 Flash | Muse Spark Open | Beam 1.0 | |---|---|---|---| | Cheapest first-party API | Yes, $0.15 / $0.60 off-peak | No first-party price confirmed | No official price confirmed | | Largest total parameters | No (552B reported) | Yes (816B) | No (501B) | | Fewest confirmed active parameters | Not confirmed | No (42B) | Yes (23B) | | Audio and video input | Not confirmed | Yes | Not confirmed | | Apache 2.0 weights | Not confirmed | No, Meta’s own license (see text) | Yes | | Newest checkpoint date | Oct. 17, 2026 | Oct. 15, 2026 | Oct. 16, 2026 | The scorecard uses “not confirmed” wherever our sources did not state a fact. That is deliberate. An empty cell is more honest than a guess, and it flags exactly what you should check on the model card before committing a budget. ## Release Timeline: What Shipped and When The order of events matters because each release reacted to the one before it. DeepSeek moved first on September 10, 2026, when it released DeepSeek-V4.1-Flash with native multimodal support, a new architecture aimed at faster inference, and reduced API prices. The new prices took effect at 04:00 UTC the same day. Four days later DeepSeek changed how its older tier behaves. Starting at 04:00 UTC on September 14, all `deepseek-v4-pro` requests route to V4.1-Flash at V4.1-Flash rates, and DeepSeek’s own notice says that continues until V4.1-Pro launches. If you still call V4 Pro by name, you are already getting Flash. The [output price cut on V4.1 Flash](https://shattered.io/deepseek-v4-1-flash-70-percent-price-cut-2026/) is the reason the swap is cheaper for most callers. Reflection announced Beam on October 5, 2026, as its first open-weight model, a sparse mixture-of-experts system for coding, reasoning and agentic workloads. Meta followed on October 15 with Muse Spark Open, described as the first open-weight model in the Muse Spark family. Reflection then published the Beam weights on October 16, along with the Reflection API and a beta of its Mirror CLI coding agent. DeepSeek followed with the V4.1-Flash-1017 checkpoint and opened its Search and Fetch APIs to outside developers. DeepSeek’s site reportedly identifies V4.1 Flash-1017 as the current version, while the public API name stays `deepseek-flash`. The 1017 label names the checkpoint, not a separate product. | Date (2026) | Event | Who | |---|---|---| | Sept. 10 | DeepSeek-V4.1-Flash released, API prices cut | DeepSeek | | Sept. 14 | `deepseek-v4-pro` requests route to V4.1-Flash | DeepSeek | | Oct. 5 | Beam announced as first open-weight model | Reflection | | Oct. 15 | Muse Spark Open released | Meta | | Oct. 16 | Beam 1.0 weights, Reflection API, Mirror CLI beta | Reflection | | Oct. 17 | V4.1-Flash-1017 checkpoint, Search and Fetch APIs | DeepSeek | | Oct. 21 | Microsoft Foundry post lists Muse Spark Open and Beam | Microsoft | | Nov. 5 | Reflection publishes an open-model safety follow-up on Beam | Reflection | Two caveats on the timeline. First, Reflection’s technical report on Beam was still described as “coming” on its Hugging Face page as of November 6, so treat any claim that cites the report as premature. Second, Meta’s wider Muse line has moved on. The company announced Muse Wonder and Muse Wonder Full for agents on October 12, and our sources do not confirm those as open-weight. Muse Spark Open is the open-weight entry. ## Specs Table: Parameters, Context, Formats and Access The table below uses only confirmed or clearly labeled figures. Where the official pages and our fact check were silent, the cell says so rather than filling a gap with an estimate. | Spec | DeepSeek V4.1 Flash | Muse Spark Open | Beam 1.0 | |---|---|---|---| | Maker | DeepSeek | Meta (Meta Superintelligence Labs) | Reflection | | Initial release | Sept. 10, 2026 | Oct. 15, 2026 | Announced Oct. 5, weights Oct. 16, 2026 | | Current checkpoint | V4.1-Flash-1017 (Oct. 17) | Muse-Spark-Open-816B-A42B | Beam-1.0-501B-A23B | | Architecture | Multimodal mixture-of-experts | Mixture-of-experts | Sparse mixture-of-experts | | Total parameters | 552B (reported in model documentation) | 816B | 501B | | Active parameters per token | Not confirmed in our sources | 42B | 23B | | Context window | 1M tokens (reported) | 1M tokens | 1M tokens | | Input modalities | Native multimodal (visual understanding) | Text, image, video, audio | Positioned for coding and agents, no multimodal claim confirmed | | Output | Not detailed in our sources | Text | Not detailed in our sources | | Weight formats | Not detailed in our sources | BF16, MXFP4, FP8, NVFP4 | BF16, FP8, NVFP4 | | Tool or function calling | Search and Fetch APIs added Oct. 17 | Yes, plus configurable reasoning | Positioned for tool use and agents | | API identifier | `deepseek-flash` | `Muse-Spark-Open-816B-A42B` | `Beam-1.0-501B-A23B` | | Official license statement | Check model card | Meta’s own license, terms covered in text | Apache 2.0 | Read the active-parameter row carefully. A mixture-of-experts model only fires a fraction of its experts for each token, so the 42B figure for Muse Spark Open and the 23B figure for Beam describe compute per token, not memory. You still have to store every expert. That is why a 501B model and an 816B model remain hard to self-host even though their per-token math looks modest. ## Architecture: Active Parameters and Mixture-of-Experts Design All three models use a mixture-of-experts design. A router picks a small group of expert networks for each token, so the model carries a huge parameter count but spends only a slice of it on any single step. That is how an 816B system can respond at a speed closer to a much smaller dense model. ### Why active parameters matter for speed and cost Meta lists 42 billion active parameters for Muse Spark Open. Reflection lists 23 billion active for Beam, out of roughly 501 billion total. On paper that gives Beam the lightest per-token compute of the two, which should help latency and serving cost when the same hardware runs both. DeepSeek documents a 552-billion-parameter backbone for V4.1 Flash, but the sources we checked did not give a confirmed active count, so a direct per-token comparison with DeepSeek is not possible yet. Reflection’s own material adds a useful detail about how Beam was trained. Its technical write-up says expert utilization is near-uniform, with the busiest expert’s load averaging 1.04 times the mean across MoE layers at the end of pretraining. Even load across experts is what lets a sparse model keep every GPU busy at serving time. It is a vendor claim, but it fits the model’s positioning as an efficient coder, and the [Beam efficiency claims](https://shattered.io/beam-open-weight-efficiency-race-2026/) are worth reading against it. ### Memory is the real constraint Active parameters cut compute. They do not cut storage. At 16-bit precision each parameter takes two bytes, so the weights alone pass a terabyte for all three models before you add the key-value cache that a one-million-token context demands. Quantized formats change that picture. Meta ships Muse Spark Open in BF16, MXFP4, FP8 and NVFP4, and Reflection ships Beam in BF16, FP8 and NVFP4. The NVFP4 builds are the realistic starting point for anyone without a rack of accelerators. Meta also makes a notable local-hardware claim. It says Muse Spark Open is optimized to run on two Apple M5 Ultra Mac Studios using MLX. That is a vendor statement we could not independently test, but it shows Meta wants this model reachable beyond data centers. Treat it as a ceiling for enthusiasts, not a production plan. ## Context Windows and Modalities Compared On context length the three are effectively tied. Meta states a one-million-token window for Muse Spark Open. Reflection’s model card lists the same for Beam, and OpenRouter shows Beam accepting up to 1,048,576 tokens per request. DeepSeek V4.1 Flash is reported at one million tokens in third-party documentation. Single-source listings also report a 384,000-token maximum output for the 1017 checkpoint, so check the provider docs before you design around long generations. Modalities are where the models separate. Muse Spark Open accepts text, images, video and audio and returns text. It also exposes configurable reasoning and tool or function calling. DeepSeek moved V4.1 Flash to native multimodal support through its API with the September 10 release, which covers visual understanding. For Beam, our sources describe a coding and agent model and make no multimodal claim, so plan on text and code only unless Reflection says otherwise. Language coverage deserves a flag too. According to the model card summary we found, Beam was developed predominantly for English and has a June 30, 2026 knowledge cutoff. That is a single-source detail, so confirm it on the repository. If your product serves non-English users, test every candidate on your own prompts before trusting a long-context headline. A one-million-token window is a ceiling, not a promise of quality at that length. Retrieval accuracy usually drops as prompts approach the limit, and serving cost climbs with every extra token held in cache. Run needle-in-a-haystack style checks at the context size you actually plan to use. A model that handles 100,000 tokens cleanly may still stumble at 900,000. ## Licensing: Apache 2.0 vs the Muse Model License Licensing is the least glamorous row in the comparison and the one most likely to stop a launch. Beam’s weights ship under Apache 2.0, and an AWS announcement dated October 16 confirms it. Apache 2.0 allows commercial use, modification and redistribution with notice and patent terms, which is why legal teams tend to approve it without a long review. Muse Spark Open uses the Muse Model License. According to Meta’s announcement, that license lets many developers use the model for free while imposing limited commercial terms. Read the phrase “limited commercial terms” as a flag, not a verdict. We did not confirm the thresholds, so check the license text for user-count or revenue triggers, acceptable-use rules and any restrictions on training other models with the outputs. For DeepSeek V4.1 Flash, the license was not confirmed in the sources we verified. Do not assume it matches an earlier DeepSeek release. Open the model card for the exact checkpoint you plan to download, and keep a copy of the license text with your deployment records. Our broader [license comparison across six open-weight families](https://shattered.io/open-weight-models-license-comparison-6-families-16gb-2026/) covers how to document that step. | Licensing question | DeepSeek V4.1 Flash | Muse Spark Open | Beam 1.0 | |---|---|---|---| | Named license | Not confirmed here | Meta’s own license (see text) | Apache 2.0 | | Free for many developers | Check model card | See text | Yes | | Commercial limits | Check model card | See text | None beyond Apache 2.0 notice terms | | Legal review effort | Unknown until license is read | Plan a full read | Usually quick | ## Benchmarks: What Is Verified and What Is Not This is the section where most comparison pages overreach, so here is the plain state of the evidence. As of November 6, 2026, we could not confirm an independent leaderboard that scores DeepSeek V4.1 Flash-1017, Muse Spark Open and Beam 1.0 on the same harness. Reflection says it designed Beam to compete with large open-weight models on coding and reasoning, but the evidence does not establish an independently verified ranking against the other two. What exists instead is a patchwork. Meta’s release page references Terminal-Bench 2.1 with the Terminus 2 agent and DeepSWE 1.1 with a best-available or self-reported harness. Reflection publishes training and evaluation write-ups, and it put out a safety follow-up on November 5. DeepSeek’s pages carry scores for earlier V4 Flash builds, including a vision experiment that the company said brought its multimodal agent results close to a closed frontier model. None of these numbers share a harness, so putting them in one column would mislead you. | Evidence type | DeepSeek V4.1 Flash | Muse Spark Open | Beam 1.0 | |---|---|---|---| | Vendor release page benchmarks | Published for earlier V4 Flash builds | Terminal-Bench 2.1, DeepSWE 1.1 referenced | Coding and reasoning positioning | | Independent same-harness ranking of all three | None confirmed | None confirmed | None confirmed | | Technical report | Not applicable here | Not applicable here | Still described as coming | | Third-party hosting listings | Yes, several | Yes, several providers | Yes, OpenRouter and others | Until a neutral lab publishes a three-way table, the defensible move is to run your own. The harness in the migration section takes about an afternoon to set up. If you want a view of how earlier open models stacked up on shared tests, the roundup of the [best open source LLMs from October](https://shattered.io/best-open-source-llm-october-2026-kimi-glm-deepseek/) shows the wider field these three just joined. ### How to run a fair three-way test A fair test needs the same prompts, the same sampling settings and the same scoring rubric for every model. Split your prompts into task families that match your product, such as bug fixing, long-document question answering, structured extraction and tool-calling sequences. Keep each family to at least a dozen prompts so one lucky answer cannot swing the result. Measure four things per family: task success, latency, tokens consumed and the number of retries needed. Token use matters because a model that writes longer answers can cost more per task even at a lower list price. Record failures as well as scores, since the way a model fails often decides whether you can trust it in production. ## Pricing: Token Costs for DeepSeek, Muse Spark Open and Beam Open weights are free to download but never free to run, so price comparisons need two layers: the hosted per-token rate and the hardware bill if you self-host. Start with the hosted layer. DeepSeek publishes the only first-party price list in this group, and it uses a peak and off-peak schedule. Off-peak rates are half the peak rates, according to DeepSeek’s own announcement. | Model and channel | Input (per 1M tokens) | Cached input (per 1M) | Output (per 1M) | Note | |---|---|---|---|---| | DeepSeek V4.1 Flash, off-peak | $0.15 | $0.003 | $0.60 | Off-peak is 50% of peak | | DeepSeek Search and Fetch API | $0.75 per 1,000 successful calls (reported) | | | Billing began around Nov. 1 | | Beam 1.0 on OpenRouter | $0.25 | Not listed | $1.00 | 1,048,576-token context, listed Oct. 16 | | Beam 1.0 first-party API | No official public price confirmed | | | Reflection API exists | | Muse Spark Open weights | Free download, license terms apply | | | Hosting cost is yours or a provider’s | The Muse Spark Open row needs context. Meta’s announcement names seven API providers: Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. Prices vary by host. One listing, Cloudflare’s page for its Fireworks-served deployment dated November 4, shows $1.25 per million input tokens, $0.15 per million cached input tokens and $4.25 per million output tokens. That is a single-source figure for one provider, so use it as a reference point, not as a standard rate. Beam pricing also depends on the host. [OpenRouter’s Reflection page](https://openrouter.ai/reflection) shows $0.25 input and $1 output per million tokens for Beam 1.0 501B A23B. Reported prices across OpenRouter hosts range from $0.25 to $0.40 for input and $1.00 to $1.20 for output, depending on provider. Those wider numbers are unofficial, and Reflection has not published its own API price in the sources we checked. ### A worked example on listed prices Per-million-token rates show the spread. On DeepSeek’s off-peak rates, input is $0.15 and output is $0.60. On OpenRouter’s Beam listing, input is $0.25 and output is $1. On the single Cloudflare listing for Muse Spark Open, input is reportedly $1.25 and output is $4.25. Those rates are simple to compare, and they show why DeepSeek’s schedule appeals to batch jobs that can wait for off-peak hours. Scale changes the picture, so here is the same arithmetic at three daily volumes with a 5-to-1 input-to-output ratio and no caching. The table covers the two models with confirmed, non-provider-specific price points. | Daily volume (input / output) | DeepSeek V4.1 Flash, off-peak | Beam 1.0 on OpenRouter | Monthly gap (30 days) | |---|---|---|---| | 1M / 0.2M tokens | $0.27 | $0.45 | $5.40 | | 10M / 2M tokens | $2.70 | $4.50 | Not listed | | 100M input tokens a day | $27.00 | $45.00 | Not listed | At 100 million input tokens a day, Beam costs more per month than DeepSeek at off-peak rates, since Beam’s OpenRouter input rate is $0.25 per million tokens against DeepSeek’s $0.15. That gap is real but small next to an engineer’s salary, so for many teams license clarity or coding quality will outweigh it. If you mostly run at peak hours, the gap narrows because DeepSeek’s rates double. Peak hours erase part of that lead. A single-source summary puts the peak input rate at $0.30 and the peak output rate at $1.20 per million tokens, which is simply double the off-peak figures. Third-party reports say peak pricing runs on weekdays during two UTC windows, but we could not verify the schedule, so confirm it on DeepSeek’s pricing page. For deeper provider-by-provider fee math, see our breakdown of [OpenRouter, Together and Fireworks pricing](https://shattered.io/openrouter-pricing-vs-together-ai-vs-fireworks-ai-5-5-percent-fee-2026/). ## Hardware and Self-Hosting Requirements Self-hosting is where the headline parameter counts bite. Even with only 23 or 42 billion parameters active per token, you load every expert into accelerator memory, plus the cache for long prompts. For most teams that means a multi-GPU node and a serving stack such as vLLM, not a single workstation card. Vendor support reflects that reality. Meta’s [Muse Spark Open model page](https://dev.meta.ai/models/muse-spark-open) sits alongside launch-week support announcements. Single-source reports say support landed for NVIDIA GB300 systems, AMD Instinct MI355X and MI350X with ROCm and vLLM, and Intel platforms, and Intel announced day-one support on November 5. Beam is published in BF16, FP8 and NVFP4, and Reflection’s download commands cover each variant plus another build named DFlash2 in its documentation. The NVFP4 files are the smallest, but they also demand hardware that handles that format well. DeepSeek V4.1 Flash is the odd one out. Most readers will use it through the API. That removes the hardware question but adds a dependency on DeepSeek’s routing decisions, such as the September 14 change that points V4 Pro traffic at Flash. If you need guarantees about where inference runs, host a model you control. | Deployment path | DeepSeek V4.1 Flash | Muse Spark Open | Beam 1.0 | |---|---|---|---| | First-party API | Yes, `deepseek-flash` | Via Meta channels and partners | Reflection API | | Third-party hosted | Several directories and hosts | Several hosted providers | OpenRouter and cloud marketplaces | | Cloud model catalogs | Not detailed in our sources | See text | SageMaker JumpStart, Foundry | | Desktop option | Not applicable | Two M5 Ultra Mac Studios with MLX (Meta claim) | Not confirmed | | Quantized builds | Not detailed | MXFP4, FP8, NVFP4 | FP8, NVFP4 | If you plan to run any of these locally, pick your runtime early. The comparison of [Ollama, LM Studio, llama.cpp and vLLM](https://shattered.io/ollama-vs-lm-studio-vs-llama-cpp-vs-vllm-local-llm-runtimes-2026/) explains why a model at this scale pushes you toward vLLM-class serving rather than a desktop app. ## Real-World Deployments Already Live Benchmarks are thin, but deployment evidence is plentiful. These are the concrete examples we could confirm as of November 6, 2026. - **AWS for Beam.** Beam became available through Amazon SageMaker JumpStart on October 16, and an AWS blog post describes it as an open-weight model for agentic coding. - **Microsoft Foundry.** A[Microsoft Foundry post dated October 21](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/now-in-foundry-meta-muse-spark-open-nvidia-nemotron-3-5-super-vl-and-reflection-/4565899) lists Muse Spark Open and Reflection Beam together, and it describes the Muse checkpoint with its 816B total, 42B active, one-million-token and multimodal specs. - **Fireworks serverless.** Fireworks announced Muse Spark Open on its US-only serverless API on October 20 and pointed buyers to its model page for current pricing. - **Cloudflare AI.** Cloudflare’s documentation lists a Fireworks-served Muse Spark Open deployment, updated in early November, with the token prices quoted above. - **Intel day-one support.** Intel said on November 5 that it supports Muse Spark Open from the first day, describing it as an 816-billion-parameter open-weight multimodal model. - **DeepSeek’s V4 Pro reroute.** Since September 14,`deepseek-v4-pro` traffic runs on V4.1 Flash at Flash rates. Every app that kept the old model name is now a live Flash deployment, whether its owners noticed or not. - **DeepSeek Search and Fetch.** Opened to outside developers on October 17, with a free window that ended around November 1 and reported billing of $0.75 per 1,000 successful calls afterward. The pattern is clear. Meta’s model spread fastest through inference vendors and chip makers, Reflection’s through cloud marketplaces and the OpenRouter listing, and DeepSeek’s through its own API and its default routing. For a closer look at Meta’s launch, our coverage of [Muse Spark Open’s 816B debut](https://shattered.io/meta-muse-spark-open-816b-params-2026/) tracks the first week of rollout. Two lessons follow from that list. First, a model’s reach in its first three weeks depends on partners as much as quality, and Meta and Reflection both lined up cloud and chip-vendor support before release day. Second, the safest procurement path is the one that keeps your options open. A cloud catalog entry, an OpenRouter listing and a first-party API all route to the same weights, so you can start on a hosted endpoint and move to your own hardware later without changing the model. ## Use-Case Recommendations: Which Model for Which Job Match the model to the job, not to the leaderboard. These six picks follow the confirmed specs and prices, and each one names the main thing to verify. ### 1. High-volume chat, summarization and classification Choose DeepSeek V4.1 Flash. The $0.15 input and $0.60 output off-peak rates make it the lowest-cost hosted option we could confirm. Schedule batch work off-peak, and keep latency-sensitive traffic on a separate budget line. Verify the license and your data-residency rules before you send sensitive text to any third-party API. ### 2. Video, audio and image understanding Choose Muse Spark Open. It is the only model here with confirmed audio and video input alongside text and images. Meeting transcription with visual context, media review and support-ticket screenshots all fit. Budget for a hosted provider, since the self-hosting footprint is the largest in the group. ### 3. Coding agents you plan to ship inside a product Choose Beam 1.0. Apache 2.0 licensing, positioning for software engineering and tool use, and the lowest confirmed active-parameter count make it the cleanest fit. Pair it with Reflection’s beta Mirror CLI for trials, and read the [Beam partner and license details](https://shattered.io/reflection-beam-open-source-apache-2-0-40-partners-2026/) before you commit. ### 4. Search-augmented research agents Choose DeepSeek if you want search and page fetching as first-party tools. The Search and Fetch APIs opened on October 17. Account for the per-call charge that started in early November when you model costs. ### 5. Regulated or redistribution-heavy products Choose Beam when you must ship weights to customers or fine-tune and redistribute. Apache 2.0 removes the guesswork. For Muse Spark Open, have counsel read the Muse Model License first. For DeepSeek, check your organization’s policy on model provenance and hosting location, since that is a governance call that no spec sheet answers. ### 6. Enthusiast and lab experiments Try Muse Spark Open on a pair of M5 Ultra Mac Studios with MLX if you own them, since Meta says it optimized for that setup. Otherwise rent a hosted endpoint for an afternoon and test all three on the same 50 prompts. For a wider view of how these releases compare with rivals like Qwen and Mistral, see the [DeepSeek, Qwen and Mistral comparison](https://shattered.io/deepseek-vs-qwen-vs-mistral-open-weight-models-2026/). ### Governance checklist before production - **Read the license text.** Apache 2.0 for Beam, Meta’s own terms for Muse Spark Open, and the model card for DeepSeek. - **Record the checkpoint, not just the alias.** The`deepseek-flash` alias follows the newest build, so log the date of the checkpoint behind each result. That makes regressions traceable. - **Decide where inference may run.** Hosted APIs send prompts to a third party. Self-hosting does not. - **Run your own safety pass.** Reflection published an open-model safety follow-up on November 5, but vendor write-ups do not replace red-teaming against your own abuse cases. - **Plan for provider drift.** Prices, routing and free windows change, as DeepSeek’s Search and Fetch billing change in early November showed. - **Keep a fallback.** Hold a second model warm so an outage or policy change does not stop your product. ## Pros and Cons of Each Model Every strength below ties to a confirmed fact. Every weakness is either a confirmed limit or a gap in public evidence, and the text says which. The broader field of [AI and machine learning releases](https://shattered.io/category/ai-machine-learning/) moves fast, so expect some of these gaps to close within weeks. ### DeepSeek V4.1 Flash-1017 - **Pro:** lowest confirmed first-party price, at $0.15 input and $0.60 output per million tokens off-peak. - **Pro:** native multimodal support through the API, with cached input at $0.003 off-peak. - **Pro:** Search and Fetch APIs give agents first-party web tools. - **Con:** peak pricing is double, and the peak schedule needs checking on DeepSeek’s pricing page. - **Con:** license and weight availability for this checkpoint were not confirmed in our verified sources. - **Con:** traffic routing can change under you, as the September 14 V4 Pro reroute showed. ### Muse Spark Open - **Pro:** text, image, video and audio input in one open-weight model, with configurable reasoning and tool calling. - **Pro:** the widest launch distribution, with seven API providers named in Meta’s announcement and several cloud and chip-vendor integrations. - **Pro:** multiple precision formats, from BF16 down to NVFP4. - **Con:** the largest footprint at 816B total parameters, so self-hosting needs serious hardware. - **Con:** a custom license with limited commercial terms that legal teams must read. - **Con:** hosted prices vary by provider and run higher than DeepSeek’s in the one listing we found. ### Beam 1.0 - **Pro:** Apache 2.0 license, confirmed by an AWS announcement. - **Pro:** 23B active parameters, the lowest confirmed per-token compute of the three. - **Pro:** clear coding-agent positioning, with the Reflection API and Mirror CLI beta alongside the weights. - **Con:** no official public API price in our sources, and third-party prices differ by host. - **Con:** no confirmed multimodal input, and a technical report still marked as coming. - **Con:** no independent ranking against rivals yet. If you also weigh Qwen, the[Muse Spark Open and Qwen3.8 matchup](https://shattered.io/muse-spark-open-vs-qwen3-8-mcp-atlas-89-2-vs-84-5-2026/) adds a useful data point. ## Migration Guide: Moving Between These Models All three can sit behind OpenAI-style chat endpoints, which makes switching a configuration job more than a rewrite. The pitfalls live in model names, prompt tuning and cost assumptions. Follow these steps in order. 1. **Inventory your model names.** If your code calls`deepseek-v4-pro` , it already runs on V4.1 Flash. Reported legacy names such as`deepseek-v4-flash` and`deepseek-v4-flash-vision-exp` route to the 1017 checkpoint at Flash pricing. Switch to`deepseek-flash` so your config states what actually runs. 2. **Freeze a test set.** Collect 50 to 100 real prompts, including your longest ones and your ugliest edge cases. Save current outputs as a baseline. 3. **Swap the endpoint, not the code.** Change the base URL, key and model identifier per target. Beam’s identifier is`Beam-1.0-501B-A23B` . For Muse Spark Open, use the identifier your chosen provider lists, because it differs by host. 4. **Re-tune prompts.** Reasoning settings, tool schemas and system prompts rarely transfer unchanged. Muse Spark Open exposes configurable reasoning, so test low and high settings separately. 5. **Test long context at your real size.** Do not trust the one-million-token label. Probe at the lengths your product uses. 6. **Model cost twice.** Run the same traffic through peak and off-peak DeepSeek rates, then through the provider rates for the other two. 7. **Ship behind a router.** Send 5 percent of traffic to the new model first, compare quality and spend, then ramp. ### A minimal side-by-side harness The script below runs the same prompts against all three targets and logs latency and token usage. Fill the Muse provider values from whichever host you chose. It uses the standard OpenAI Python client. ``` python import json, os, time from openai import OpenAI TARGETS = { "deepseek": ("https://api.deepseek.com", "deepseek-flash", "DEEPSEEK_API_KEY"), "beam": ("https://api.reflection.ai/openai/v1", "Beam-1.0-501B-A23B", "REFLECTION_API_KEY"), "muse": (os.environ.get("MUSE_BASE_URL", ""), os.environ.get("MUSE_MODEL_ID", ""), "MUSE_API_KEY"), } prompts = [json.loads(line) for line in open("prompts.jsonl")] results = [] for name, (base_url, model, key_env) in TARGETS.items(): if not base_url: continue client = OpenAI(base_url=base_url, api_key=os.environ[key_env]) for p in prompts: start = time.time() r = client.chat.completions.create( model=model, messages=[{"role": "user", "content": p["prompt"]}], max_tokens=1024, ) results.append({ "target": name, "id": p["id"], "seconds": round(time.time() - start, 2), "in_tokens": r.usage.prompt_tokens, "out_tokens": r.usage.completion_tokens, "text": r.choices[0].message.content, }) json.dump(results, open("results.json", "w"), indent=2) ``` Score the saved outputs blind. Strip the target name, shuffle the answers, and have two reviewers grade them against your rubric. Then multiply the logged token counts by each channel’s rate. That gives you a quality-per-dollar number from your own workload, which no public leaderboard can supply today. ## Verdict: Which Open Model Wins on the Data On confirmed price, DeepSeek V4.1 Flash wins. Its $0.60 off-peak output rate sits below Beam’s $1 on OpenRouter, and well below the single Muse Spark Open listing we found at $4.25. That lead shrinks at peak hours, since peak rates double, and it comes with open questions on license and weight availability that you must close before building on it. On breadth, Muse Spark Open wins. It is the only model here with confirmed audio and video input, it has the biggest parameter count at 816B, and it launched with the widest provider and chip-vendor support. Its costs are the footprint and a custom license. On licensing and coding focus, Beam 1.0 wins. Apache 2.0 is the cleanest legal position of the three, and 23B active parameters suggest efficient serving. For a team building coding agents today, the default pick is Beam, with DeepSeek as the cheap fallback tier for bulk work. | If you care most about | Pick | Why | |---|---|---| | Lowest token price | DeepSeek V4.1 Flash | $0.15 input, $0.60 output off-peak | | Audio and video input | Muse Spark Open | Only confirmed multimodal breadth | | Permissive license | Beam 1.0 | Apache 2.0 | | Lowest active parameters | Beam 1.0 | 23B active | | Widest hosting choice | Muse Spark Open | Most hosting and chip-vendor support at launch | | Web search for agents | DeepSeek V4.1 Flash | Search and Fetch APIs | The wider shift matters as much as the winner. Meta’s 816B release and Reflection’s Apache 2.0 weights give buyers who prefer U.S.-based vendors real options, while DeepSeek keeps the price floor low. Competition on three axes at once, price, license and modality, is good news for anyone who buys tokens. It also raises the cost of standing still. A team that locked in one vendor in August now faces a market where the cheapest option, the most capable multimodal option and the most permissive option are three different models. Build your stack so the model is a configuration value. That single design choice turns every future release into a one-afternoon experiment instead of a migration project, and it is the best hedge against open weight models changing again before the year ends. Three things would change this verdict. A V4.1-Pro launch would end DeepSeek’s temporary routing and could reset its price story. Reflection’s technical report, still marked as coming, would give outsiders numbers to test. And an independent same-harness leaderboard that includes all three would replace the careful hedging in this comparison with real rankings. ## Frequently Asked Questions ### Which is the best open-weight model right now: DeepSeek V4.1 Flash, Muse Spark Open or Beam? It depends on the job. DeepSeek V4.1 Flash has the lowest confirmed price, Muse Spark Open has the broadest modality support, and Beam 1.0 has the clearest license. No independent ranking of all three exists yet, so test on your own prompts. ### Is DeepSeek V4.1 Flash open-weight? Our verified sources confirm DeepSeek’s API release and its documentation of a 552-billion-parameter backbone. They did not confirm the weight license or download availability for the 1017 checkpoint. Read the model card before you plan a self-hosted deployment. ### What is the difference between V4.1 Flash and V4.1-Flash-1017? V4.1 Flash is the model released on September 10, 2026. The 1017 label marks the checkpoint DeepSeek released on October 17. The public API name for both is `deepseek-flash`, so callers get the newest build without changing code. ### How much memory does Muse Spark Open need? Meta lists 816 billion total parameters, so weights alone pass a terabyte at 16-bit precision. Quantized NVFP4 and MXFP4 builds shrink that. Meta also says the model is optimized for two Apple M5 Ultra Mac Studios running MLX, which is a vendor claim we could not test. ### Can I use Beam 1.0 commercially? Reflection released the weights under Apache 2.0, which permits commercial use with the license’s notice and patent terms. Keep a copy of the license with your deployment records and confirm the terms on the official repository. ### Did DeepSeek retire V4 Pro? Not according to the evidence we found. Since September 14, requests to `deepseek-v4-pro` route to V4.1 Flash at Flash rates until V4.1-Pro launches. That is a temporary reroute, not a confirmed retirement. ### Where can I run Muse Spark Open without owning hardware? Meta’s announcement names Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI as API providers. Pricing differs by host, so compare current rates on each provider’s model page before you commit. ### Should I wait for a three-way benchmark before choosing? No. A short test on your own 50 to 100 prompts will tell you more than a leaderboard built on someone else’s tasks. Use the harness above, score the outputs blind, and revisit the choice when independent results land.