MiMo-V2.6-Pro vs Kimi K3.1 Pro
MiMo-V2.6-Pro and Kimi K3.1 Pro are within three points on the Models at Work leaderboard, which we treat as a tie (76 vs 75). MiMo-V2.6-Pro is about 6.8× cheaper per blended million tokens ($0.54 vs $3.68, one figure an Artificial Analysis estimate). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 46 to 30.
| MiMo-V2.6-Pro · Xiaomi | Kimi K3.1 Pro · Moonshot | |
|---|---|---|
| Leaderboard rank | #8 | #10 |
| Score (0–100) | 76 | 75 |
| Tier | Open weights | Open weights |
| Price per 1M tokens | $0.43 in · $0.87 out | $3.68 blended (estimate) |
| Context window | 1M | 1M |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 30 | 46 |
| Arena Text score | 1480 (rank 26) | — |
| Arena WebDev score | 1629 (rank 19) | — |
| Terminal-Bench 4.0 (vendor) | — | 65.7% |
| Best for | Self-hosted agentic coding, Cheap frontier-class batch reasoning, Replacing closed mid-tier models | Self-hosting at scale, Open-weights requirements, Long-context agent work |
Compare source-qualified API rates and a monthly workload example before estimating your bill.
Choose MiMo-V2.6-Pro if…
- you need self-hosted agentic coding
- you need cheap frontier-class batch reasoning
- you need replacing closed mid-tier models
The strongest open-weight model right now and absurdly cheap for what it does. MIT-licensed 1T MoE that lands within a few points of Muse Spark and Grok 4.7. Slow-ish and verbose, so budget for latency and thinking tokens.
Caveats: Overthinks: long, repetitive reasoning traces on simple edits; AA flags it as verbose vs the median model. ~47 tok/s on the first-party API is slow next to Gemini Flash or Haiku-class models. Only 4k Arena votes so far; the rank is still settling.
Choose Kimi K3.1 Pro if…
- you need self-hosting at scale
- you need open-weights requirements
- you need long-context agent work
Open weights, 1M context and 46 on the independent index, which puts a self-hostable model within a few points of Gemini 3.8 Flash-class hosted tiers; the catch is a 2.8T-parameter footprint.
Caveats: Weights are under a custom licence with revenue conditions (Moonshot's Hugging Face card), so check it before calling this open source. Price is Artificial Analysis' blended figure, not a vendor list price; Moonshot's sources read gave no API price. A LocalLLaMA poster argues K3.1 is benchmark-tuned; that is one user's claim, not a finding. The only benchmark Moonshot cites (65.7% Terminal-Bench 4.0) is vendor-run and not counted.
What practitioners say
MiMo-V2.6-Pro: HN (1,130 points) was won over less by scores than by Xiaomi's transparency: a live training dashboard with loss curves, dropped datasets and running cost estimates. Users call it 'incredibly cheap' for Muse-Spark-level benchmarks and say it keeps DeepSeek and GLM 'in check'. Complaints are consistent: 'faster than DeepSeek but still much slower than leading models', and it 'seems to overthink way too much', producing huge reasoning traces for trivial edits. Several recommend it via OpenRouter rather than the Chinese first-party API.
Kimi K3.1 Pro: Early and split: a Hacker News thread on the open-weights release weighs hardware needs, benchmarks and cost, and a LocalLLaMA poster argues the model is benchmark-tuned, citing an audit PDF. Too early to score; sentiment drops out.
Questions people ask
Which is better, MiMo-V2.6-Pro or Kimi K3.1 Pro?
MiMo-V2.6-Pro and Kimi K3.1 Pro are within three points on the Models at Work leaderboard, which we treat as a tie (76 vs 75). MiMo-V2.6-Pro is about 6.8× cheaper per blended million tokens ($0.54 vs $3.68, one figure an Artificial Analysis estimate). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 46 to 30.
When should I choose MiMo-V2.6-Pro over Kimi K3.1 Pro?
Choose MiMo-V2.6-Pro for self-hosted agentic coding, cheap frontier-class batch reasoning, replacing closed mid-tier models. The strongest open-weight model right now and absurdly cheap for what it does. MIT-licensed 1T MoE that lands within a few points of Muse Spark and Grok 4.7. Slow-ish and verbose, so budget for latency and thinking tokens.
When should I choose Kimi K3.1 Pro over MiMo-V2.6-Pro?
Choose Kimi K3.1 Pro for self-hosting at scale, open-weights requirements, long-context agent work. Open weights, 1M context and 46 on the independent index, which puts a self-hostable model within a few points of Gemini 3.8 Flash-class hosted tiers; the catch is a 2.8T-parameter footprint.
Which is cheaper, MiMo-V2.6-Pro or Kimi K3.1 Pro?
MiMo-V2.6-Pro costs $0.43 per million input tokens and $0.87 per million output tokens on Xiaomi's list price (MiMo-V2.6-Pro - Artificial Analysis, read Nov 9, 2026). Moonshot does not publish a simple list price for Kimi K3.1 Pro; Artificial Analysis estimates a blended $3.68 per million tokens (three input to one output).
Scores come from the Models at Work leaderboard: Score is 0 to 100 and computed, not typed: 55% independent evals (Artificial Analysis, Arena, Scale SEAL, Epoch, each scaled against its natural floor and the best score in this table, then averaged), 15% blended price on a fixed log scale ($0.05 per million is 100, $60 is 0), 15% practitioner sentiment from Signals and community threads, 15% operational fit (context window, open weights). A missing component drops out and the rest are reweighted. A model with no independent eval yet is provisional and ranks below every measured one. Within three points is a tie. Vendor-published figures are labelled and never counted. Prices are vendor list prices where published; blended figures assume three input tokens per output token.