text model comparison
MiMo-V2.6-Flash vs MiniMax M3.1 Flash
MiMo-V2.6-Flash and MiniMax M3.1 Flash are both current text models, from Xiaomi MiMo and MiniMax. MiMo-V2.6-Flash is 7% cheaper per 1M input tokens than MiniMax M3.1 Flash, $0.14 vs $0.15. Both accept 1,000,000 tokens of context.
- Lower price per 1M input tokens
- MiMo-V2.6-Flash$0.14 against $0.15
- Newer release
- MiniMax M3.1 FlashOct 2026 against Sep 2026
Data verified October 23, 2026 · Dataset 2026-10-23
Side by side
MiMo-V2.6-Flash and MiniMax M3.1 Flash, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | MiMo-V2.6-Flash | MiniMax M3.1 Flash |
|---|---|---|
| Price | ||
| Price per 1M input tokens | $0.14 (better) | $0.15 |
| Price per 1M output tokens | $0.28 (better) | $0.60 |
| Independent benchmarks | ||
| Arena Text | 1451 Elo (#35 of 49) | Not measured |
| Specs | ||
| Provider | Xiaomi MiMo | MiniMax |
| Release date | September 22, 2026 | October 19, 2026 |
| Access | API and open weights | API and open weights |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | Not reported |
| Input | text, image, audio, video | text, image, video |
| Output | text | text |
No independent benchmark has scored both models yet. Each rank counts the current models scored on that benchmark.
Scores:Arena · 2026-10-12
Charts
Against every current text model
Where MiMo-V2.6-Flash and MiniMax M3.1 Flash land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
Only MiMo-V2.6-Flash has a current Arena Text score: #35 of 49. Each bar is the share of current models it beats.
Independent benchmarks
Arena Text
MiMo-V2.6-Flash:#35of 49
1,451 Elo · ahead of 29%
MiniMax M3.1 Flash: Not scored
Price and context
Price per 1M input tokens
MiMo-V2.6-Flash:#4of 49
$0.14 · cheaper than 94%
MiniMax M3.1 Flash:#5of 49
$0.15 · cheaper than 90%
Price per 1M output tokens
MiMo-V2.6-Flash:#1of 49
$0.28 · cheaper than 100%
MiniMax M3.1 Flash:#6of 49
$0.60 · cheaper than 90%
Context window
MiMo-V2.6-Flash:#26of 64
1M tokens · longer than 22%
MiniMax M3.1 Flash:#26of 64
1M tokens · longer than 22%
Quality against price
MiMo-V2.6-Flash scores 1,451 at $0.175 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.
Quality against price: the data
| Model | Provider | Blended price per 1M tokens | Arena Text (Elo) |
|---|---|---|---|
| Gemini 4 Argon | $4 | 1,525 | |
| Claude Opus 5.5 | Anthropic | $8 | 1,507 |
| Claude Fable 5 | Anthropic | $20 | 1,504 |
| Claude Fable 5.1 | Anthropic | $20 | 1,501 |
| Gemini 3.8 Flash | $1.50 | 1,497 | |
| Muse Spark 1.3 | Meta | $2 | 1,494 |
| Claude Opus 5 | Anthropic | $10 | 1,490 |
| Kimi K3 | Moonshot Kimi | $6 | 1,488 |
| Gemini 3.7 Flash | $1.50 | 1,486 | |
| GPT-5.6 Sol | OpenAI | $8 | 1,485 |
| GPT-6.1 Sol | OpenAI | $4 | 1,484 |
| Qwen3.8-Max | Alibaba Qwen | $3 | 1,483 |
| GPT-5.5 | OpenAI | $11 | 1,482 |
| Gemini 3.6 Flash | $3 | 1,482 | |
| Claude Opus 4.8 | Anthropic | $10 | 1,481 |
| MiMo-V2.6-Pro | Xiaomi MiMo | $0.54 | 1,480 |
| GLM-5.3 | Z.ai | $2.15 | 1,478 |
| Claude Sonnet 5.5 | Anthropic | $4 | 1,476 |
| GPT-5.4 | OpenAI | $5.63 | 1,475 |
| GPT-6 Astra | OpenAI | $20 | 1,475 |
| DeepSeek-V4.1-Flash | DeepSeek | $0.52 | 1,475 |
| GLM-5.3-Flash | Z.ai | $0.24 | 1,475 |
| Gemini 3 Flash Preview | $1.13 | 1,473 | |
| Grok 4.20 | xAI | $1.56 | 1,472 |
| GPT-5.6 Terra | OpenAI | $4.50 | 1,466 |
| Grok 4.5 | xAI | $3 | 1,466 |
| DeepSeek-V4-Pro | DeepSeek | $1.98 | 1,465 |
| Claude Sonnet 5 | Anthropic | $4 | 1,461 |
| GPT-6 Sol | OpenAI | $4 | 1,456 |
| Gemini 3.5 Flash-Lite | $0.85 | 1,455 | |
| Grok 4.6 | xAI | $3 | 1,454 |
| GPT-5.6 Luna | OpenAI | $0.45 | 1,452 |
| MiMo-V2.6-Flash | Xiaomi MiMo | $0.18 | 1,451 |
| GPT-6 Luna | OpenAI | $0.20 | 1,443 |
| Grok 4.7 | xAI | $3 | 1,443 |
| Grok 4.3 | xAI | $1.56 | 1,442 |
| Mistral Large 4 | Mistral AI | $2.06 | 1,429 |
| GPT-4.1 | OpenAI | $3.50 | 1,415 |
| Solar Pro 4 | Upstage | $0.52 | 1,386 |
| GPT-4.1 mini | OpenAI | $0.70 | 1,383 |
| GPT-4.1 nano | OpenAI | $0.18 | 1,322 |
Cost
What each costs
Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.
The same work, priced
For 10M input and 2M output tokens, MiniMax M3.1 Flash costs 1.4 times as much as MiMo-V2.6-Flash, at each provider's listed rates.
10M input and 2M output tokens
MiniMax M3.1 Flash costs 1.4× as much
- MiMo-V2.6-Flash
- $1.96
- MiniMax M3.1 Flash
- $2.70
The same, with 9M of the input tokens cached
MiniMax M3.1 Flash costs 2.0× as much
- MiMo-V2.6-Flash
- $0.73
- MiniMax M3.1 Flash
- $1.49
| Workload or rate | MiMo-V2.6-Flash | MiniMax M3.1 Flash |
|---|---|---|
| Example workloads | ||
| 10M input and 2M output tokens | $1.96 (better) | $2.70 |
| The same, with 9M of the input tokens cached | $0.73 (better) | $1.49 |
| The same, through the batch API | $0.98 | Not listed |
| Other listed rates | ||
| 1M input tokens, cached | $0.0028 (better) | $0.015 |
| 1M input tokens, batch | $0.07 | Not listed |
| 1M output tokens, batch | $0.14 | Not listed |
Verdict
When to choose each
MiMo-V2.6-Flash
Where it leads
- Lower input and output prices, $0.14 and $0.28 vs $0.15 and $0.60 per 1M tokens
- Accepts audio input
- Cheaper cached input, $0.0028 vs $0.015 per 1M tokens
- Batch pricing, $0.07 input and $0.14 output per 1M tokens
Best for
- High-frequency multimodal calls
- Low-cost agent workloads
Watch out for
- Xiaomi's own table puts it well behind the Pro model on exploitation tasks (ExploitBench 25.3 against 47.9)
- The API checkpoint (RL or MOPD weights) is not identified
MiniMax M3.1 Flash
Where it leads
No measured lead over MiMo-V2.6-Flash in this data.
Best for
- Low-cost coding and agent loops
- Long-context multimodal work on a budget
Watch out for
- Thinking cannot be turned off; effort runs from low to max, and max is the default
- MiniMax gives two parameter counts (about 428B in the model card, 220B in the file metadata), and no independent benchmark score is recorded here yet
From benchmark to production
Building on MiMo-V2.6-Flash or MiniMax M3.1 Flash?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
FAQ
MiMo-V2.6-Flash vs MiniMax M3.1 Flash FAQ
Which is cheaper, MiMo-V2.6-Flash or MiniMax M3.1 Flash?
MiMo-V2.6-Flash is 7% cheaper per 1M input tokens than MiniMax M3.1 Flash, $0.14 vs $0.15. For 10M input and 2M output tokens, MiMo-V2.6-Flash costs $1.96 and MiniMax M3.1 Flash $2.70 at standard rates.
Which is cheaper with prompt caching, MiMo-V2.6-Flash or MiniMax M3.1 Flash?
MiMo-V2.6-Flash charges $0.0028 per 1M cached input tokens, 98% below its standard input rate, and MiniMax M3.1 Flash $0.015, 90% below. With 9M of 10M input tokens cached and 2M output tokens, MiMo-V2.6-Flash costs $0.73 and MiniMax M3.1 Flash $1.49.
Which has the larger context window, MiMo-V2.6-Flash or MiniMax M3.1 Flash?
Both accept 1,000,000 tokens of context.
How do MiMo-V2.6-Flash and MiniMax M3.1 Flash differ in features?
MiMo-V2.6-Flash takes text, image, audio and video input; MiniMax M3.1 Flash takes text, image and video.
Further reading
More on MiMo-V2.6-Flash and MiniMax M3.1 Flash
Related comparisons
- GLM-5.3-Flash vs MiMo-V2.6-Flash
- GLM-5.3-Flash vs MiniMax M3.1 Flash
- GPT-4.1 nano vs MiMo-V2.6-Flash
- GPT-5.6 Luna vs MiniMax M3.1 Flash
- GPT-5.6 Luna vs MiMo-V2.6-Flash
- Mercury 2.5 vs MiniMax M3.1 Flash
- GPT-6 Luna vs MiMo-V2.6-Flash
- Mercury 3 vs MiniMax M3.1 Flash
- MiMo-V2.6-Flash vs MiMo-V2.6-Pro
- MiniMax M3.1 Flash vs Qwen3.8-Omni-Flash
- Claude Haiku 5.5 vs MiMo-V2.6-Flash
- Mercury 2.5 vs MiMo-V2.6-Flash
Production evaluation
Choosing between MiMo-V2.6-Flash and MiniMax M3.1 Flash?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty
