GPT-6.1 Sol vs MiMo-V2.6-Pro
GPT-6.1 Sol and MiMo-V2.6-Pro are within three points on the Models at Work leaderboard, which we treat as a tie (81 vs 81). MiMo-V2.6-Pro is about 7.4× cheaper per blended million tokens ($0.54 vs $4). On Artificial Analysis' independent Intelligence Index, GPT-6.1 Sol leads 52 to 46.
| GPT-6.1 Sol · OpenAI | MiMo-V2.6-Pro · Xiaomi | |
|---|---|---|
| Leaderboard rank | #6 | #7 |
| Score (0–100) | 81 | 81 |
| Tier | Workhorse | Open weights |
| Price per 1M tokens | $2 in · $10 out | $0.43 in · $0.87 out |
| Context window | 1M | 1M |
| Open weights | No | Yes |
| Artificial Analysis Intelligence Index | 52 | 46 |
| Humanity's Sixth Sense (Scale) | 46.6 | — |
| Arena Text score | — | 1480 (rank 26) |
| Arena WebDev score | — | 1629 (rank 19) |
| Best for | OpenAI-standardised teams, Agents API and Decisions API, Cost-controlled reasoning | Self-hosted agentic coding, Cheap frontier-class batch reasoning, Replacing closed mid-tier models |
Choose GPT-6.1 Sol if…
- you need openai-standardised teams
- you need agents api and decisions api
- you need cost-controlled reasoning
Near-Astra intelligence at a fifth of the price, which is OpenAI's own line and, for once, the index agrees.
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
GPT-6.1 Sol: The launch thread's title did the arguing: 'near-Astra for a fifth of the price' drew 955 comments, most of them about routing.
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, GPT-6.1 Sol or MiMo-V2.6-Pro?
GPT-6.1 Sol and MiMo-V2.6-Pro are within three points on the Models at Work leaderboard, which we treat as a tie (81 vs 81). MiMo-V2.6-Pro is about 7.4× cheaper per blended million tokens ($0.54 vs $4). On Artificial Analysis' independent Intelligence Index, GPT-6.1 Sol leads 52 to 46.
When should I choose GPT-6.1 Sol over MiMo-V2.6-Pro?
Choose GPT-6.1 Sol for openai-standardised teams, agents api and decisions api, cost-controlled reasoning. Near-Astra intelligence at a fifth of the price, which is OpenAI's own line and, for once, the index agrees.
When should I choose MiMo-V2.6-Pro over GPT-6.1 Sol?
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, GPT-6.1 Sol or MiMo-V2.6-Pro?
GPT-6.1 Sol costs $2 per million input tokens and $10 per million output tokens on OpenAI's list price (OpenAI API pricing, read Oct 11, 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 11, 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.