MiMo-V2.6-Pro vs DeepSeek V4.1 Flash (1017)
MiMo-V2.6-Pro and DeepSeek V4.1 Flash (1017) are within three points on the Models at Work leaderboard, which we treat as a tie (81 vs 80). They cost about the same per million tokens ($0.54 vs $0.52 blended). On Artificial Analysis' independent Intelligence Index, MiMo-V2.6-Pro leads 46 to 45.
| MiMo-V2.6-Pro · Xiaomi | DeepSeek V4.1 Flash (1017) · DeepSeek | |
|---|---|---|
| Leaderboard rank | #7 | #9 |
| Score (0–100) | 81 | 80 |
| Tier | Open weights | Fast and cheap |
| Price per 1M tokens | $0.43 in · $0.87 out | $0.30 in · $1.20 out |
| Context window | 1M | 1M |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 46 | 45 |
| Arena Text score | 1480 (rank 26) | — |
| Arena WebDev score | 1629 (rank 19) | — |
| Best for | Self-hosted agentic coding, Cheap frontier-class batch reasoning, Replacing closed mid-tier models | Cheap coding tiers, Bulk generation, Experiments where data residency is not a constraint |
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 DeepSeek V4.1 Flash (1017) if…
- you need cheap coding tiers
- you need bulk generation
- you need experiments where data residency is not a constraint
Now measured: 45 on the Artificial Analysis index at 227 tokens/s and open weights, the fastest model near the top of the board.
Caveats: Weights are MIT-licensed on Hugging Face per DeepSeek; the 1017 snapshot's Terminal-Bench 4.0 figure is DeepSeek's own and is not counted. One independent index so far (Artificial Analysis 45, max setting); no Arena or Scale entry yet. Off-peak and peak pricing differ; the cheap numbers are off-peak. Read data-handling terms before enterprise use.
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.
DeepSeek V4.1 Flash (1017): The 1,067-point HN thread on the earlier Flash release said the price is real and the coding gap to Opus is also real; nothing newer read this run, so sentiment is unscored.
Questions people ask
Which is better, MiMo-V2.6-Pro or DeepSeek V4.1 Flash (1017)?
MiMo-V2.6-Pro and DeepSeek V4.1 Flash (1017) are within three points on the Models at Work leaderboard, which we treat as a tie (81 vs 80). They cost about the same per million tokens ($0.54 vs $0.52 blended). On Artificial Analysis' independent Intelligence Index, MiMo-V2.6-Pro leads 46 to 45.
When should I choose MiMo-V2.6-Pro over DeepSeek V4.1 Flash (1017)?
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 DeepSeek V4.1 Flash (1017) over MiMo-V2.6-Pro?
Choose DeepSeek V4.1 Flash (1017) for cheap coding tiers, bulk generation, experiments where data residency is not a constraint. Now measured: 45 on the Artificial Analysis index at 227 tokens/s and open weights, the fastest model near the top of the board.
Which is cheaper, MiMo-V2.6-Pro or DeepSeek V4.1 Flash (1017)?
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 19, 2026). DeepSeek V4.1 Flash (1017) costs $0.30 per million input tokens and $1.20 per million output tokens on DeepSeek's list price (DeepSeek API pricing, read Oct 19, 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.