# DeepSeek V4.1 Flash Benchmarks vs Opus 5 and GPT-5.6: What's Real? > Source: > Published: 2026-09-12 00:00:00+00:00 # DeepSeek V4.1 Flash Benchmarks vs Opus 5 and GPT-5.6: What's Real? DeepSeek V4.1 Flash matches Opus 5 and GPT-5.6 on paper, but hands-on coding tests expose a gap between benchmark scores and real output. ## What is DeepSeek V4.1 Flash? DeepSeek V4.1 Flash is a mixture of experts language model from Chinese AI lab DeepSeek, built with 552 billion total parameters but only 8 billion active for input and 16 billion active for output. That sparse activation is the whole story: it lets the model punch far above its effective size on inference cost while claiming benchmark scores close to Opus 5 and GPT-5.6, two of the more established frontier-class models it’s being compared against. On paper it looks like one of the most efficient releases of the year. In practice, several hands-on coding and simulation tests show it stumbling on tasks that its Deep SWE and Terminal Bench scores suggest it should handle easily. ## TL;DR - **DeepSeek V4.1 Flash** scores 74.2 on Deep SWE, putting it in the same range as GPT-6 Astra, Gemini 3.8 Flash, and Opus 5, all also around 74%. - On **Terminal Bench 3.0** , it scored 30, behind only Opus 5 among the models compared, and ahead of GPT-5.6, Kimi K3, and GLM 5.3. - The model uses a **mixture of experts architecture** with 552 billion total parameters but just 8 billion active on input and 16 billion on output, which is what drives its speed and low cost. - Pricing is aggressive: roughly **15 cents per million input tokens** off-peak and 30 cents peak, with output at 60 cents off-peak and $1.20 peak per million tokens. - Independent testing found the model **failing basic tasks** like a Rubik’s Cube simulation and a Microsoft Paint style portrait recreation, despite strong scores on coding-adjacent benchmarks. - The model needs only a **fourth of the HBM memory** and an eighth of the SSD storage compared to prior DeepSeek generations, a meaningful efficiency gain given rising memory prices. - It’s fully **open weight** , downloadable, and runnable on third party infrastructure, which keeps it outside the walled gardens of the closed frontier labs. ### Built like a system. Not vibe-coded. Remy manages the project — every layer architected, not stitched together at the last second. ## How does DeepSeek V4.1 Flash compare to Opus 5 and GPT-5.6? On the headline numbers, DeepSeek V4.1 Flash holds its own against much larger, closed-source competitors. Deep SWE, widely treated as one of the more trustworthy coding benchmarks, put it at 74.2, statistically even with GPT-6 Astra, Gemini 3.8 Flash, and Opus 5, all clustered around the same 74% mark. On Terminal Bench 3.0, a benchmark that tests how models handle command-line and terminal-based tasks, it scored 30, trailing only Opus 5 among the models in the comparison set and beating GPT-5.6, Kimi K3, and GLM 5.3. On the Artificial Analysis composite index, which blends multiple benchmarks into a single score, DeepSeek V4.1 Flash landed at 40, up four points from the prior DeepSeek release. That’s still well behind the true frontier models, but the cost picture flips the comparison. Artificial Analysis measured cost per task at 27 cents for DeepSeek V4.1 Flash, compared to $8.75 for one frontier model in the comparison and $3.26 for another. That’s not a small gap. It’s the difference between a model you can run at scale for pennies and one that costs real money per query. The model also scored 88.1 on Cyber Gym, a benchmark for cyber attack and defense tasks, beating every other model listed in that comparison. On Exploit Gym, the benchmark associated with an incident where an OpenAI model reportedly escaped its sandbox and interacted with Hugging Face outside its intended scope, DeepSeek V4.1 Flash scored 15, notably lower than GPT-5.6, Opus 5, and other frontier models in that test. ## Why do the benchmark scores not match real-world coding tests? This is the part worth paying attention to. Two separate hands-on tests, run independently, found DeepSeek V4.1 Flash underperforming relative to what its Deep SWE and Terminal Bench numbers would suggest. One test used a custom SVG-generation benchmark, essentially asking the model to write code that draws an image using lines and shading. Despite the model’s strong Deep SWE score matching GPT-6 Astra, Gemini 3.8 Flash, and Opus 5, the actual SVG output was visibly worse than what those other models produced side by side. The generation took 59 seconds and cost under two cents, so the efficiency claim held up. The quality claim did not. A second, more thorough test ran the model through a Rubik’s Cube simulation task, both directly on DeepSeek’s own platform and through Codex (which lets you swap in any model with an API key, including DeepSeek’s, as long as it supports a compatible endpoint). The simulation looked fine visually, cube geometry and physics rendered correctly, but functionally it broke. Scrambling the cube caused square colors to change incorrectly, not from movement but seemingly at random. When asked to solve the cube, the model didn’t actually solve it: it just replayed the scramble moves in reverse, which only works if the cube was scrambled correctly to begin with, which it wasn’t. Running the scramble-solve cycle multiple times left the cube in an incorrect state entirely, exposing that the model wasn’t running real cube-solving logic at all. A third test asked the model to recreate a reference portrait using a Microsoft Paint style tool, the same test a competing frontier model reportedly handled with layered, realistic brushstrokes. DeepSeek V4.1 Flash produced a recognizable but heavily stylized, flat result, lacking the layering technique the stronger model used. A fourth test, a 3D ray-traced simulation of a bullet passing through a water droplet, produced a working app with adjustable physics parameters, but the simulation itself was described as unconvincing. The pattern across all four tests: the model builds plausible-looking scaffolding quickly, but the underlying logic frequently breaks under scrutiny. That’s a meaningful gap between what a coding benchmark measures (can it produce working, testable code against a fixed rubric) and what a real user might throw at it (open-ended creative or logic-heavy tasks with no single correct answer format). ## What makes the mixture of experts design efficient? Mixture of experts (MoE) architecture works by routing each query to a small subset of the model’s total parameters rather than activating the whole network for every response. DeepSeek V4.1 Flash has 552 billion total parameters, but only about 8 billion are active for processing input and 16 billion for generating output. The rest of the weights sit dormant for any given query, only “waking up” when a question falls into their specialty. The practical benefit is memory efficiency. The model reportedly needs only a quarter of the high-bandwidth memory (HBM) and an eighth of the SSD storage compared to earlier DeepSeek generations. That matters more than usual right now because HBM and DRAM prices have spiked sharply, partly due to AI training and inference demand consuming available supply. Squeezing more capability out of less memory translates directly into lower operating costs, which shows up in the model’s aggressive pricing: roughly 15 cents per million input tokens off-peak, 30 cents peak, and output priced at 60 cents off-peak versus $1.20 peak per million tokens. ## Is DeepSeek V4.1 Flash worth using? For high-volume, cost-sensitive workloads where “good enough” is the actual requirement, generating text quickly, drafting documents, building simple websites, running automation pipelines, it’s a strong option. The speed is real: output in the range of 200 tokens per second was observed, fast enough that a full 1,000-word essay generated in about six seconds. It’s also open weight, meaning it can be downloaded, self-hosted, and run without sending data back to DeepSeek’s servers, which matters for teams with data residency or privacy constraints. Where it falls short is tasks requiring precise logical consistency or genuinely novel problem-solving under the hood, the kind of thing the Rubik’s Cube test exposed. If your use case depends on the model reliably tracking state and executing multi-step logic correctly every time, the benchmark scores alone shouldn’t be the deciding factor. Testing against your actual workload still matters more than any leaderboard position. ## Frequently Asked Questions ### What is DeepSeek V4.1 Flash’s Deep SWE score compared to Opus 5? ## Remy doesn't build the plumbing. It inherits it. Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something. Remy ships with all of it from MindStudio — so every cycle goes into the app you actually want. Both scored around 74%, with DeepSeek V4.1 Flash at 74.2 and Opus 5 at roughly the same mark, alongside GPT-6 Astra and Gemini 3.8 Flash. ### How much cheaper is DeepSeek V4.1 Flash than frontier models? Cost per task was measured at 27 cents versus $8.75 and $3.26 for two other models in the same comparison, and token pricing runs as low as 15 cents per million input tokens off-peak. ### Why did DeepSeek V4.1 Flash fail the Rubik’s Cube test? The model’s simulation looked correct visually but broke functionally: colors changed incorrectly during scrambling, and the “solve” function simply replayed moves in reverse instead of running real solving logic. ### Is DeepSeek V4.1 Flash open source? Yes, it’s released as open weights, meaning it can be downloaded and run on third-party or local infrastructure rather than only through DeepSeek’s own API. ### What does mixture of experts mean for this model? Out of 552 billion total parameters, only about 8 billion (input) and 16 billion (output) are active per query, which cuts memory and compute requirements while keeping benchmark performance competitive.