Fastest LLM 2026: Mercury 2.5 Beats Luna and Haiku

Inception Labs' Mercury 2.5 is the fastest LLM available via API as of September 2026, posting 1,107 tokens per second on vendor benchmarks and 440 tok/s P50 at 1.17s latency in OpenRouter telemetry, according to a comparison of leading fast models. The diffusion-based model is priced at $0.20/$0.75 per million input/output tokens, undercutting rivals including Gemini 3.5 Flash-Lite, GPT-5.6 Luna, and Claude Haiku 4.5 on output cost, though GPT-5.6 Luna remains the pick for cheap long-context general chat. Inception Labs, founded in 2024 by Stefano Ermon, Aditya Grover, and Volodymyr Kuleshov, has raised a $50M seed led by Menlo Ventures and has a larger model in training.

Verdict: Inception Labs' Mercury 2.5 is the fastest LLM you can call from an API in September 2026 — 1,107 tokens per second https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 on widely available NVIDIA GPUs, at a list price of $0.20/$0.75 per million tokens https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 that undercuts every rival here on output. For latency-bound work voice agents, search pipelines, coding subagents , Mercury wins. For cheap general-purpose chat with long context, GPT-5.6 Luna https://developers.openai.com/api/docs/models/gpt-5.6-luna.md still wins. Claude Haiku 4.5 and Gemini 3.5 Flash-Lite are squeezed between the two. Mercury 2.5 wins this comparison by both vendor figure and third-party measurement. Inception Labs reports 1,107 tokens per second and calls it the most capable diffusion LLM on the market, with a 40% intelligence gain over Mercury 2 https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 . OpenRouter's telemetry for the endpoint shows 440 tok/s P50 at 1.17 s latency https://openrouter.ai/inception/mercury-2.5-preview . Both numbers sit above every measured rival. The split: | Model | Measured output speed | Input $/M | Output $/M | Context | |---|---|---|---|---| | Mercury 2.5 | 440 tok/s OpenRouter P50 ; 1,107 tok/s vendor-reported | $0.20 | $0.75 | 260,000 | | Gemini 3.5 Flash-Lite | 382 tok/s Artificial Analysis | $0.30 | $2.50 | Long-context | | GPT-5.6 Luna | 129 tok/s Artificial Analysis, max | $0.20 | $1.20 | Long-context | | Claude Haiku 4.5 | ~82 tok/s Artificial Analysis, non-reasoning | $1.00 | $5.00 | Long-context | Sources: Inception Labs https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 , OpenRouter https://openrouter.ai/inception/mercury-2.5-preview , OpenAI developer docs https://developers.openai.com/api/docs/models/gpt-5.6-luna.md , Google DeepMind model cards https://deepmind.google/models/gemini/ , Anthropic pricing https://www.anthropic.com/pricing . Read the table on the axis your workload cares about. On output price, Haiku 4.5 at $5.00 per million costs about 6.7x Mercury 2.5 DeepMind model card https://deepmind.google/models/gemini/ ; Anthropic pricing https://www.anthropic.com/pricing . On measured speed, Mercury's 440 tok/s OpenRouter P50 is roughly 5x Haiku's 82 tok/s and 3.4x Luna's 129 tok/s OpenRouter https://openrouter.ai/inception/mercury-2.5-preview ; Artificial Analysis https://artificialanalysis.ai/models/claude-4-5-haiku . Flash-Lite is the only rival close to Mercury on speed, and it charges 3.3x more per million output tokens Google DeepMind model card https://deepmind.google/models/gemini/ . Mercury is a diffusion language model, not an autoregressive one. Models like Luna and Haiku emit one token at a time, so latency scales with output length. A diffusion LLM refines a block of tokens in parallel across denoising steps, which decouples throughput from sequence length — and that is where the 1,107 tok/s figure comes from Inception Labs https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 . Inception shipped the first commercial diffusion LLM, Mercury, in February 2025, then Mercury 2 in February 2026 https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 . The company was founded in 2024 by Stefano Ermon Stanford , Aditya Grover UCLA and Volodymyr Kuleshov Cornell , and raised a $50M seed led by Menlo Ventures https://techcrunch.com/2025/11/06/ with Microsoft's M12, NVIDIA's NVentures and angel cheques from Andrew Ng and Andrej Karpathy. A larger model is already in training. If you would rather compare fast open-weight options, our GLM-5.3 Flash vs Qwen 3.8 Flash-Next vs DeepSeek V4 Flash comparison https://dev.to/articles/glm-5-3-flash-vs-qwen-3-8-flash-next-vs-deepseek-v4-flash covers that tier, and Qwen 3.8 Flash-Next versus the larger Qwen variants https://dev.to/articles/qwen-3-8-flash-next-vs-qwen-3-8-27b-125b-6b-active sets out the tradeoff inside one family. Two vendor-published customer figures: Augment Code reports compaction latency fell from 150 seconds to 27 seconds, an 82% reduction with cost down 90% https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 ; voice-agent company OpenCall reports median response latency around 170 ms with P99 dropping from minutes to about one second. Both are vendor numbers, not independent benchmarks — treat them as directional. OpenRouter's traffic pattern fits that story: the top consumers of the endpoint are agentic clients — Hermes Agent at 3.86B tokens, Claude Code at 2.3B https://openrouter.ai/inception/mercury-2.5-preview — a workload shape where a coding tool fires many short internal calls and the user waits on the sum. Four caveats: For the frontier tiers, see our GPT-5 versus Claude 4 verdict https://dev.to/articles/gpt-5-vs-claude-4-2026-verdict , the Claude Fable 5.1 pricing comparison https://dev.to/articles/claude-fable-5-1-vs-fable-5-pricing-effort-comparison-2026 , and the best local LLM for coding https://dev.to/articles/best-local-llm-for-coding-qwen3-8-27b-vs-glm-5-3-vs-gemma-4 . A one-afternoon evaluation: Q: What is the fastest LLM in 2026? A: Mercury 2.5, reported at 1,107 tokens/sec Inception https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 , observed at 440 tok/s P50 OpenRouter https://openrouter.ai/inception/mercury-2.5-preview . Next best: Flash-Lite 382, Luna 129 Artificial Analysis https://artificialanalysis.ai/models/gpt-5-6-luna . Q: Is Mercury 2.5 cheaper than GPT-5.6 Luna? A: Input matches at $0.20 Inception https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 ; OpenAI https://developers.openai.com/api/docs/models/gpt-5.6-luna.md ; Mercury output is $0.75 vs $1.20. Luna's bigger context window is the counterweight for long documents. Q: Why is Claude Haiku 4.5 still worth using? A: Not for throughput. At 82 tok/s and $1.00/$5.00 per million Artificial Analysis https://artificialanalysis.ai/models/claude-4-5-haiku it is the slowest, priciest option here — worth it only when you want Anthropic's instruction-following in a small model. Q: Does a diffusion LLM handle tool calling and agents? A: Yes — tool calling, parallel tool calls, structured outputs and tunable reasoning through an OpenAI-compatible API, and agentic clients dominate its OpenRouter traffic. Q: Is the $0.04 per million input price still available? A: No. The 80% launch discount closed 8 September 2026 https://openrouter.ai/inception/mercury-2.5-preview . List price is $0.20/$0.75 per million Inception Labs https://www.inceptionlabs.ai/blog/introducing-mercury-2-5 . Q: What is the catch with the 1,107 tokens per second figure? A: It is vendor-reported from Inception's own hardware. Third-party observation on OpenRouter is well below it, so benchmark your own prompts before planning capacity. No corrections yet. Speed and price figures were last verified against primary vendor documentation on 10 September 2026 and will be rechecked when any of the four models ships a new revision.