Kimi K3.1 Pro vs MiMo-V2.6-Pro
Kimi K3.1 Pro scores higher on the Models at Work leaderboard (86 vs 81 out of 100). MiMo-V2.6-Pro is about 7.4× cheaper per blended million tokens ($0.54 vs $4). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 55 to 46.
| Kimi K3.1 Pro · Moonshot | MiMo-V2.6-Pro · Xiaomi | |
|---|---|---|
| Leaderboard rank | #2 | #8 |
| Score (0–100) | 86 | 81 |
| Tier | Open weights | Open weights |
| Price per 1M tokens | $2 in · $10 out | $0.43 in · $0.87 out |
| Context window | 1M | 1M |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 55 | 46 |
| Terminal-Bench 4.0 (vendor) | 65.7% | — |
| Arena Text score | — | 1480 (rank 26) |
| Arena WebDev score | — | 1629 (rank 19) |
| Best for | Agentic coding with an open-weight exit, 1M-token long-context work, Moving off Kimi K3 | Self-hosted agentic coding, Cheap frontier-class batch reasoning, Replacing closed mid-tier models |
Compare source-qualified API rates and a monthly workload example before estimating your bill.
Choose Kimi K3.1 Pro if…
- you need agentic coding with an open-weight exit
- you need 1m-token long-context work
- you need moving off kimi k3
Fourth on the Artificial Analysis index at 55, one point behind Sonnet 5.5, at the same $2 in / $10 out list price, with the weights still to come.
Caveats: Scores within three points of Sonnet 5.5 (tie), and ranks on evals and price alone because no practitioner sentiment exists yet. Moonshot says the full weights arrive by 4 Nov 2026; until then it is open-weight in intent, hosted-only in practice. Artificial Analysis rates it slow (51 tokens/s) and very verbose: 150M tokens to run its index, which is where the $2.11 per-task cost comes from. Moonshot's Terminal-Bench figure is vendor-run and is not counted. No Arena or Scale result yet, and no practitioner threads in Signals, so sentiment is left out.
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.
What practitioners say
Kimi K3.1 Pro: No practitioner threads recorded in Signals yet; left out of the score rather than guessed.
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.
Questions people ask
Which is better, Kimi K3.1 Pro or MiMo-V2.6-Pro?
Kimi K3.1 Pro scores higher on the Models at Work leaderboard (86 vs 81 out of 100). MiMo-V2.6-Pro is about 7.4× cheaper per blended million tokens ($0.54 vs $4). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 55 to 46.
When should I choose Kimi K3.1 Pro over MiMo-V2.6-Pro?
Choose Kimi K3.1 Pro for agentic coding with an open-weight exit, 1m-token long-context work, moving off kimi k3. Fourth on the Artificial Analysis index at 55, one point behind Sonnet 5.5, at the same $2 in / $10 out list price, with the weights still to come.
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.
Which is cheaper, Kimi K3.1 Pro or MiMo-V2.6-Pro?
Kimi K3.1 Pro costs $2 per million input tokens and $10 per million output tokens on Moonshot's list price (Kimi API model pricing, read Oct 26, 2026). 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 Oct 26, 2026).
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.