Appearance
On September 22, two frontier models shipped on the same day from opposite corners of the industry. Xiaomi's MiMo-V2.6-Pro reached 46 on the Artificial Analysis Intelligence Index, the highest any open-weight model has posted there. SpaceXAI's Grok 4.7 landed on the same 46. Same number on the scoreboard. One costs $0.13 per Intelligence Index task to evaluate. The other costs $3.74. Run the full evaluation and the gap widens to $206.66 versus $4,967.35.
That 29x gap is the defining fact of this release cycle. It changes which model a rational team defaults to.
The same week, Alibaba announced Qwen 4 at the Apsara Conference, and Reuters reported plans for a model in the 5 trillion to 10 trillion parameter range backed by a new in-house chip. The open-weight frontier didn't just move. It split.
Same score, 29x the price
The Artificial Analysis Intelligence Index composites reasoning, coding, agentic tool use and knowledge work into one number. MiMo-V2.6-Pro's 46 puts it above Kimi K3 and Qwen3.8 Max, and Grok 4.7 sits at the same level. Both still trail Claude Fable 5.1 and GPT-6 Astra, but at the top the open-versus-closed line has all but dissolved.
| Model | Weights | Intelligence Index | Cost per task | Full eval run |
|---|---|---|---|---|
| MiMo-V2.6-Pro | Open | 46 | $0.13 | $206.66 |
| Grok 4.7 (xhigh) | Closed | 46 | $3.74 | $4,967.35 |
The two models don't address the same market anymore. Grok 4.7 brings a 500K context window, up from 256K in 4.6; MiMo runs 1M native. Grok is text-first, aimed at programming and long knowledge work. MiMo is natively omnimodal: text, image, video and audio in one checkpoint, with 3D spatial reasoning and computer use (CUA) built in.
Under the hood, MiMo-V2.6-Pro is a sparse MoE with 309B total parameters and 15B activated. The 15B active figure means each forward pass costs roughly what a 15B dense model costs. The 309B total means you won't run it on a single GPU; the reference SGLang setup wants 8-way tensor parallelism. Plan the serving cluster before you commit.
Where the money goes: verbosity and speed
The price gap comes from model behavior, not pricing strategy. During the intelligence index evaluation, Grok 4.7 generated 240 million tokens. The median model generates 94 million. Artificial Analysis flags that as "very verbose," and verbosity is expensive when you pay per token.
Grok 4.7 (xhigh) also ran at roughly 39 tokens per second in parts of the eval. At that rate, a long answer leaves the user staring at a blinking cursor. On longer prompts it reached about 188 output tokens per second. Both figures sit well below what the current open-weight generation has made people expect.
MiMo-V2.6 kept the V2.5 pricing unchanged, so no launch discount is involved. This is structural.
| Variant | Input (cache hit) | Input (cache miss) | Output |
|---|---|---|---|
| MiMo-V2.6-Pro | ¥0.025 / M tokens | ¥3 / M tokens | ¥6 / M tokens |
| MiMo-V2.6-Flash | ¥0.02 / M tokens | ¥1 / M tokens | ¥2 / M tokens |
| Pro UltraSpeed | 10x Pro pricing | 10x Pro pricing | 10x Pro pricing |
Quick Take: Two models can post identical composite scores while differing 29x in operating cost, and for production teams that cost curve matters as much as the scoreboard.
What MiMo-V2.6 actually is
The Flash checkpoint's backbone: 48 layers with 39 sliding-window attention and 9 global attention blocks, hidden size 4096, 256 routed experts with 8 active. Vision arrives through a 681M-parameter MiMo ViT; audio through a 308M AudioTokenizer plus a 127M patch encoder. Context length maxes at 1M tokens, enough to hold a large repository or a multi-session agent trace without chunking. A 5-layer speculative decoder predicts seven tokens per forward pass for parallel verification.
The reward machinery deserves attention. Binary pass/fail cannot rank two passing trajectories, so Xiaomi's groupwise agentic grading compares rollouts within each group, builds task-specific rubrics offline (GRS), and redistributes advantage toward higher-quality, shorter solutions (GAR). The stated goal: steer the model toward fewer tokens per task while improving outcome quality.
The RL training story
MiMo-V2.6 uses a "You Only RL Once" design. Instead of separate RL passes per domain, coding, general agent tasks, visual and cybersecurity run in one mixed batch with multiple harnesses, so strategies transfer across domains. Training is fully asynchronous GRPO: 1,568 prompts with 16 rollouts per step, billions of tokens per update, and a frozen MoE router to limit model drift. Anti-reward-hacking measures include adversarial screening and verifier cross-checks.
The scale is the story, and Xiaomi made it public. Before release, team lead Luo Fuli live-streamed the entire training run: step counts, token consumption, task pass rates, infrastructure failures, running cost. A major Chinese open release has never done that.
Flash completed 30 RL steps in just under six days for about $850K. Pro did the same for about $2.62M. Combined: 1.505 million samples, 156.4 billion tokens, roughly $3.47M total, an average burn of $30.8K per hour. Average task pass rate climbed 25 points on Flash and 12 on Pro. DeepSWE v1.1 tells the sharper story: Flash went 48.8 to 65.7, Pro 58.4 to 72.6, against a previous-generation Pro that scored 19.0.
Key numbers
- 46. Intelligence Index score for MiMo-V2.6-Pro and Grok 4.7, identical on both sides of the open/closed divide.
- $0.13 vs $3.74. Cost per Intelligence Index task, the core of the 29x gap.
- $3.47M. Total spend for both V2.6 RL runs, Flash at ~$850K and Pro at ~$2.62M.
- 1M / 15B / 309B. Native context tokens, active parameters, total parameters.
- 240M. Tokens Grok 4.7 emitted in one eval run, against a 94M median.
Benchmarks: where the scoreboard leans
| Benchmark | MiMo Pro | MiMo Flash | Claude Opus 5 | GPT-5.6 Sol | Claude Fable 5 |
|---|---|---|---|---|---|
| DeepSWE v1.1 | 71.9 | 67.9 | 74.0 | 73.0 | 70.0 |
| AutomationBench v1.0.6 | 53.1 | 52.3 | 50.3 | 45.8 | 46.2 |
| Terminal Bench 2.1 | 89.9 | 87.6 | 89.1 | 88.8 | 84.3 |
| OSWorld-Verified | 82.0 | 80.8 | 83.4 | 83.0 | 86.0 |
| Toolathlon-Verified | 76.9 | 73.6 | 80.6 | 74.9 | 77.9 |
| JobBench | 62.0 | 61.2 | 65.7 | 45.4 | 57.4 |
The pattern: MiMo leads on AutomationBench, JobBench and Terminal Bench 2.1, the tests that reward sustained multi-step reliability. It trails on Toolathlon and OSWorld. Its weakest slot is Terminal Bench 4.0 at 34.9, against GPT-5.6's 39.9 and Claude Opus 5's 49.0. Grok 4.7's DeepSWE score is 71, slightly below MiMo-Pro's 71.9, and its Terminal Bench 4.0 mark of 38.0 ties DeepSeek V4.1 Flash.
What the Community Is Saying. The loudest reactions centered on price, but the more useful ones centered on delivery. I ran the same Three.js single-file prompt through both models: MiMo returned a complete launch site with towers, tanks and ground detail, where Grok spent a long time and handed back a twisted shape with smoke and no structure. I found MiMo-Flash on OpenCode capable but looping, executing commands and re-reading files until it hit a cap. A model can score 46 and still stall inside a tool loop. Plan for both.
The 9B distill: Qwen base, MiMo data
Xiaomi also released MiMo-V2.6-Distill-Qwen-9B, an SFT of Qwen3.5-9B on MiMo-generated data: 77.4B tokens total, 27.2B loss-bearing, covering code, cyber, general agent tasks and visual coding. Against the Qwen3.5-9B base: SWE Pro jumps 32.0 to 44.6, AutomationBench 5.0 to 30.3, Terminal Bench 2.1 27.0 to 37.1, JobBench 2.6 to 18.3. SWE Verified moves only 60.0 to 61.1.
The caveat: this checkpoint skips the RL pipeline entirely. Xiaomi frames it as a starting point for open research in agentic reinforcement learning, not as a miniature V2.6. If you need the full agentic capability, use the RL checkpoints. If you need something that fits on a single 24GB GPU and holds its own on tool use, this 9B model is the cheapest entry ticket in the release.
Qwen 4 and the 5 trillion question
Alibaba announced Qwen 4 at Apsara with few details, while Reuters reported a planned model in the 5 trillion to 10 trillion parameter range plus a new chip. That reads as a bet that raw scale still dominates the frontier. The open-weight record will move again if it ships.
The r/LocalLLaMA reception was muted for a different reason. I kept waiting for a new 35B-class MoE from Alibaba, a follow-up to the A3B footprint, and Apsara came and went without one. The thread that summed it up asked directly whether Alibaba abandoned the 35B A3B. No announcement, no small MoE. For teams that standardized on that form factor, the silence is a real signal, and the slot Alibaba left open is exactly where Xiaomi's 9B distill now sits.
Common pitfalls
- Judging Flash by Pro's numbers. Flash answers image tasks in about six seconds but with noticeably thinner content. It is built for cost and latency, not for full delivery work. Match variant to task.
- Trusting agent loops to self-heal. In OpenCode, MiMo-Flash repeatedly executed commands and re-read files without converging. Put timeouts and loop-count caps in production agent frameworks, regardless of benchmark scores.
- Misjudging the hardware envelope. The 15B active parameter count hides a 309B total. The model needs 8-way tensor parallelism (SGLang
--tp 8), and stable vLLM may lag behind the new architecture. Use the maintained images from the model card or SGLang's MiMo cookbook. - Treating the distill as a shrunken flagship. The 9B SFT checkpoint has no RL training. Its benchmark gains are real, but long-horizon agent behavior differs materially.
- Skipping retrieval verification. When I asked MiMo-Flash to build a Xiaomi car website, the copy listed Xiaomi specs while the search-embedded images were Mercedes, BMW and Audi. Web-grounded agents need a content check step before they ship.
One thing to remember: when two models tie on capability and one costs 29x less, your workload is the tiebreaker, not the leaderboard. The open-weight model's real edge is that you can run it yourself, measure it on your tasks, and tune it without a meter running.
The Bottom Line
- If you are building agent products that pay per API call, default to MiMo-V2.6-Pro. At $0.13 per Intelligence Index task, long agent runs stay affordable, and it beats the closed alternatives on AutomationBench, JobBench and Terminal Bench 2.1.
- If you are latency-constrained or serving in-house, run MiMo-V2.6-Flash or the 9B distill for interactive work, and reserve Pro for long-horizon tasks where a 64-minute generation delivers a complete artifact that Flash cannot.
- One thing to watch: Alibaba's 5T-10T parameter model and Grok 4.8 (about 2.5T parameters, a fresh C++ training stack, RL starts this week) both land within months. The cost-per-intelligence curve is still dropping, so don't lock your stack to one vendor's API pricing.