MiMo-V2.6 Pro & Flash: specs, benchmarks, pricing & API options
Xiaomi’s MiMo team shipped two models on September 21, 2026, and each one matters for a different reason:
- MiMo-V2.6-Pro is the new #1 open-weights model on the Artificial Analysis Intelligence Index: 46, ahead of GLM-5.3 and Qwen3.8 Max (45) and Kimi K3 (44), at a blended $0.18 per 1M tokens.
- MiMo-V2.6-Flash is the direct successor to MiMo v2.5: the same size class (309B total / 15B active) and the same list price, with agent-benchmark scores that land within a few points of Pro.
Both ship MIT-licensed weights, a 1M-token context window and omnimodal input (text, image, audio, video). A naming note for searchers: this is MiMo-V2.6, September 2026. It is not the older MiMo-V2-Flash from late 2025, which had a 256K context and is a different model.
MiMo-V2.6 specs: Pro vs Flash
Section titled “MiMo-V2.6 specs: Pro vs Flash”| MiMo-V2.6-Pro | MiMo-V2.6-Flash | |
|---|---|---|
| Architecture | 1.02T total / 42B active MoE | 309B total / 15B active MoE, hybrid sliding-window + global attention |
| Decoding | Reasoning model | Reasoning model, 5-layer multi-token prediction (7 tokens per pass) |
| Input | Text, image, audio, video | Text, image, audio, video |
| Context window | 1M tokens, up to 128K output | 1M tokens, up to 128K output |
| Open weights | XiaomiMiMo/MiMo-V2.6-Pro-RL, MIT, FP8 | XiaomiMiMo/MiMo-V2.6-Flash-RL, MIT, BF16 + FP8 |
| List price (API) | $0.435 in / $0.87 out per 1M, cached input $0.0036 | $0.14 in / $0.28 out per 1M, cached input $0.0028 |
| Measured speed | 124.5 output tok/s, 2.34s to first token (AA) | Not yet measured independently |
The “-RL” suffix in the repository names confuses people. These are full checkpoints from the final reinforcement-learning run, not adapters. Pro and Flash are also separate training runs with their own benchmark tables, not one model cut down to two sizes.
The vendor benchmarks, charted
Section titled “The vendor benchmarks, charted”These are Xiaomi’s own reported numbers from its vendor harness, not independently reproduced. The baseline is last generation’s largest model, MiMo-V2.5-Pro:
| Benchmark (Xiaomi, vendor-run) | MiMo-V2.5-Pro | MiMo-V2.6-Flash | MiMo-V2.6-Pro |
|---|---|---|---|
| Terminal-Bench 2.1 | 65.2 | 87.6 | 89.9 |
| DeepSWE v1.1 | 19.0 | 67.9 | 71.9 |
| CyberGym | 40.0 | 95.1 | 94.0 |
| AutomationBench | 16.0 | 52.3 | 53.1 |
| MiMo Code Bench | 40.4 | 61.2 | 63.2 |
| JobBench | — | 61.2 | 62.0 |
| MiMo Visual Coding | — | 71.5 | 72.3 |
The gap between the two new models is small. Flash is within 4.0 points of Pro on every benchmark, and it beats Pro on CyberGym. The generational jump is large: on DeepSWE, Flash scores 67.9 where last generation’s Pro scored 19.0. Xiaomi credits scaled RL for this: about 750,000 trajectories across more than 7,000 environments, and it published the runs live while they trained. That jump is the headline, and it is also the reason to wait for independent reproductions before trusting every point.
Where Pro lands on the Intelligence Index
Section titled “Where Pro lands on the Intelligence Index”The independent signal comes from Artificial Analysis, using Intelligence Index v4.3.2. Every score below was checked today on that edition. AA rescored the whole field in this edition, so these numbers are not comparable with scores quoted from earlier editions.
| Model | AA Intelligence Index v4.3.2 | AA blended $/1M | AA output tok/s |
|---|---|---|---|
| GPT-6 Astra / Claude Fable 5.1 | 53 | — | — |
| Claude Opus 5 | 51 | — | 56.4 |
| MiMo-V2.6-Pro | 46 | $0.18 | 124.5 |
| GLM-5.3 (max) | 45 | $0.90 | 61.0 |
| Qwen3.8 Max | 45 | $1.18 | 39.2 |
| Kimi K3 (max) | 44 | $2.31 | 40.6 |
| DeepSeek V4.1 Flash | 39 | $0.18 | 225.6 |
Blended = AA’s 7:2:1 cache-hit/input/output mix. The full field and its history are in our monthly LLM Pareto Frontier report.
The honest read. Pro does not beat the closed frontier: GPT-6 Astra and Claude Fable 5.1 lead at 53, 7 points ahead. Its lead over the next open-weights models is a single point, which is within the noise of any index. The notable part is the rest of the row. Against the three open-weights models it edges out:
- it is 5–13× cheaper on AA’s blended price;
- it is about 2–3× faster on measured output speed.
It costs the same blended $0.18 as DeepSeek V4.1 Flash while scoring 7 points higher. That combination puts Pro on AA’s intelligence-vs-cost Pareto frontier at $0.13 per Index task.
MiMo-V2.6-Flash: a new generation at the old price
Section titled “MiMo-V2.6-Flash: a new generation at the old price”Flash has no AA score yet. Here is why it may be the more important release for high-volume workloads:
- The cost profile is unchanged. Flash keeps the exact list price of MiMo v2.5 ($0.14 in / $0.28 out, cached input $0.0028) and the same 15B-active footprint. Agent loops, sub-agents and long-context summarization get a generation’s worth of capability at the same per-token cost.
- It lands close to Pro. On Xiaomi’s own table, Flash sits within 4.0 points of the 1T Pro everywhere. At the size class, this is the part to check once independent numbers land.
- 1M context stays usable. The hybrid sliding-window + global attention keeps long inputs affordable to serve, and the 7-token multi-token-prediction head is built for fast decoding.
The weights: MIT, both of them
Section titled “The weights: MIT, both of them”Pro and Flash both ship under plain MIT on Hugging Face. Pro is in FP8; Flash is in BF16 and FP8. Alongside them, Xiaomi published:
- a technical report;
- more than 7,000 RL environments;
- its end-to-end RL framework;
- a 9B distilled model.
The full task datasets have not been released yet. For anyone evaluating models to serve rather than just call, the top of the open-weights index now carries the most permissive license there is.
Where they stand in our review
Section titled “Where they stand in our review”We serve MiMo v2.5 in the Core Pool today, next to DeepSeek V4.1 Flash, unlimited on flat monthly time blocks. MiMo-V2.6-Flash is under review as its successor in that slot. MiMo-V2.6-Pro is under evaluation. The live status of both is always on the models-under-review page, and additions land in the changelog the day they ship.
Common questions
Section titled “Common questions”What is MiMo-V2.6? Xiaomi’s September 2026 model generation, in two open-weights models. MiMo-V2.6-Pro is a 1.02T-total / 42B-active MoE. MiMo-V2.6-Flash is a 309B-total / 15B-active MoE. Both have a 1M-token context window, omnimodal input (text, image, audio, video) and MIT-licensed weights. Xiaomi also offers a Pro-UltraSpeed variant on its own API, advertised at up to 20× Pro’s output speed.
What is the difference between MiMo-V2.6-Pro and MiMo-V2.6-Flash? Size and price. Pro is about 3× larger in active parameters and costs about 3× more per token ($0.435 / $0.87 vs $0.14 / $0.28 per 1M). On Xiaomi’s vendor benchmarks, Flash sits within 4.0 points of Pro on every task and beats it on CyberGym (95.1 vs 94.0).
Is MiMo-V2.6-Pro the best open-weights model? On the Artificial Analysis Intelligence Index v4.3.2 it is #1 among open-weights models, at 46. That is one point ahead of GLM-5.3 and Qwen3.8 Max (45) and two ahead of Kimi K3 (44). It is also the cheapest and fastest of that group: $0.18/1M blended and 124.5 tok/s. GPT-6 Astra and Claude Fable 5.1 lead the overall index at 53.
How much does the MiMo-V2.6 API cost? Xiaomi lists MiMo-V2.6-Pro at $0.435 input / $0.87 output per 1M tokens, with cached input at $0.0036. MiMo-V2.6-Flash is $0.14 / $0.28, with cached input at $0.0028, which is the same as MiMo v2.5.
Does MiMo-V2.6 have open weights? Yes. Both are on Hugging Face under the MIT license: XiaomiMiMo/MiMo-V2.6-Pro-RL and XiaomiMiMo/MiMo-V2.6-Flash-RL. The “-RL” suffix marks the final RL checkpoint; these are full models, not adapters.
Is MiMo-V2.6-Flash the same as MiMo-V2-Flash? No. MiMo-V2-Flash is Xiaomi’s late-2025 model with a 256K context. MiMo-V2.6-Flash is the September 2026 successor to MiMo v2.5, with a 1M context and the RL-scaled post-training described above.
Is there an unlimited MiMo-V2.6 API subscription? Not from us today. Flash is under review as the successor to MiMo v2.5 in the Core Pool, and Pro is under evaluation (live status). MiMo v2.5 is served unlimited in the Core Pool today: flat monthly fee, no token caps during your reserved hours.
CheapestInference serves Kimi K3 and Qwen3.8 Max (Flagship Pool), GLM 5.3 and MiniMax M3 (Frontier Pool) and DeepSeek V4.1 Flash and MiMo v2.5 (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.