| Signal | MiniMax M2.7 | Delta | Mistral Large |
|---|---|---|---|
Capabilities | 67 | +17 | |
Benchmarks | 67 | -2 | |
Pricing | 99 | +5 | |
Context window size | 84 | +3 | |
Recency | 100 | +100 | |
Output Capacity | 85 | +65 | |
| Overall Result | 5 wins | of 6 | 1 wins |
Score History
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MiniMax
Mistral AI
MiniMax M2.7 saves you $410.00/month
That's $4920.00/year compared to Mistral Large at your current usage level of 100K calls/month.
| Metric | MiniMax M2.7 | Mistral Large | Winner |
|---|---|---|---|
| Overall Score | 68 | 69 | Mistral Large |
| Rank | #71 | #68 | Mistral Large |
| Quality Rank | #71 | #68 | Mistral Large |
| Adoption Rank | #71 | #68 | Mistral Large |
| Parameters | -- | -- | -- |
| Context Window | 205K | 128K | MiniMax M2.7 |
| Pricing | $0.30/$1.20/M | $2.00/$6.00/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | MiniMax M2.7 |
| Benchmarks | 67 | 69 | Mistral Large |
| Pricing | 99 | 94 | MiniMax M2.7 |
| Context window size | 84 | 81 | MiniMax M2.7 |
| Recency | 100 | 0 | MiniMax M2.7 |
| Output Capacity | 85 | 20 | MiniMax M2.7 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 68/100 (rank #71), placing it in the top 76% of all 290 models tracked.
Scores 69/100 (rank #68), placing it in the top 77% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
MiniMax M2.7 offers 81% better value per quality point. At 1M tokens/day, you'd spend $22.50/month with MiniMax M2.7 vs $120.00/month with Mistral Large - a $97.50 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. MiniMax M2.7 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (205K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (69/100) correlates with better nuance, coherence, and style in long-form content
MiniMax M2.7 and Mistral Large are extremely close in overall performance (only 1 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
MiniMax M2.7
Marginally better benchmark scores; both are excellent
Best for Cost
MiniMax M2.7
81% lower pricing; better value at scale
Best for Reliability
MiniMax M2.7
Higher uptime and faster response speeds
Best for Prototyping
MiniMax M2.7
Stronger community support and better developer experience
Best for Production
MiniMax M2.7
Wider enterprise adoption and proven at scale
by MiniMax
| Capability | MiniMax M2.7 | Mistral Large |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
MiniMax
Mistral AI
MiniMax M2.7 saves you $8.82/month
That's 82% cheaper than Mistral Large at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | MiniMax M2.7 | Mistral Large |
|---|---|---|
| Context Window | 205K | 128K |
| Max Output Tokens | 131,072 | -- |
| Open Source | Yes | No |
| Created | Mar 18, 2026 | Feb 26, 2024 |
Mistral Large scores 69/100 (rank #68) compared to MiniMax M2.7's 68/100 (rank #71), giving it a 1-point advantage. Mistral Large is the stronger overall choice, though MiniMax M2.7 may excel in specific areas like cost efficiency.
MiniMax M2.7 is ranked #71 and Mistral Large is ranked #68 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
MiniMax M2.7 is cheaper at $1.20/M output tokens vs Mistral Large's $6.00/M output tokens - 5.0x more expensive. Input token pricing: MiniMax M2.7 at $0.30/M vs Mistral Large at $2.00/M.
MiniMax M2.7 has a larger context window of 204,800 tokens compared to Mistral Large's 128,000 tokens. A larger context window means the model can process longer documents and conversations.