| Signal | Mistral Large 2407 | Delta | Qwen2.5 72B Instruct |
|---|---|---|---|
Capabilities | 50 | -- | |
Benchmarks | 55 | 0 | |
Pricing | 6 | +6 | |
Context window size | 81 | +10 | |
Recency | 42 | +11 | |
Output Capacity | 20 | -50 | |
| Overall Result | 3 wins | of 6 | 2 wins |
8
days higher
3
days
19
days higher
Mistral AI
Alibaba
Qwen2.5 72B Instruct saves you $468.50/month
That's $5622.00/year compared to Mistral Large 2407 at your current usage level of 100K calls/month.
| Metric | Mistral Large 2407 | Qwen2.5 72B Instruct | Winner |
|---|---|---|---|
| Overall Score | 56 | 56 | Mistral Large 2407 |
| Rank | #105 | #106 | Mistral Large 2407 |
| Quality Rank | #105 | #106 | Mistral Large 2407 |
| Adoption Rank | #105 | #106 | Mistral Large 2407 |
| Parameters | -- | 72B | -- |
| Context Window | 131K | 33K | Mistral Large 2407 |
| Pricing | $2.00/$6.00/M | $0.12/$0.39/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | Mistral Large 2407 |
| Benchmarks | 55 | 55 | Qwen2.5 72B Instruct |
| Pricing | 6 | 0 | Mistral Large 2407 |
| Context window size | 81 | 72 | Mistral Large 2407 |
| Recency | 42 | 31 | Mistral Large 2407 |
| Output Capacity | 20 | 70 | Qwen2.5 72B Instruct |
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 56/100 (rank #105), placing it in the top 64% of all 290 models tracked.
Scores 56/100 (rank #106), placing it in the top 64% of all 290 models tracked.
With only a 0-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.
Qwen2.5 72B Instruct offers 94% better value per quality point. At 1M tokens/day, you'd spend $7.65/month with Qwen2.5 72B Instruct vs $120.00/month with Mistral Large 2407 - a $112.35 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. Qwen2.5 72B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.39/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (56/100) correlates with better nuance, coherence, and style in long-form content
Mistral Large 2407 and Qwen2.5 72B Instruct are extremely close in overall performance (only 0.20000000000000284 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Mistral Large 2407
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen2.5 72B Instruct
94% lower pricing; better value at scale
Best for Reliability
Mistral Large 2407
Higher uptime and faster response speeds
Best for Prototyping
Mistral Large 2407
Stronger community support and better developer experience
Best for Production
Mistral Large 2407
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Mistral Large 2407 | Qwen2.5 72B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Qwen2.5 72B Instruct saves you $10.12/month
That's 94% cheaper than Mistral Large 2407 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 | Mistral Large 2407 | Qwen2.5 72B Instruct |
|---|---|---|
| Context Window | 131K | 33K |
| Max Output Tokens | -- | 16,384 |
| Open Source | No | Yes |
| Created | Nov 19, 2024 | Sep 19, 2024 |
Mistral Large 2407 scores 56/100 (rank #105) compared to Qwen2.5 72B Instruct's 56/100 (rank #106), giving it a 0-point advantage. Mistral Large 2407 is the stronger overall choice, though Qwen2.5 72B Instruct may excel in specific areas like cost efficiency.
Mistral Large 2407 is ranked #105 and Qwen2.5 72B Instruct is ranked #106 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.
Qwen2.5 72B Instruct is cheaper at $0.39/M output tokens vs Mistral Large 2407's $6.00/M output tokens - 15.4x more expensive. Input token pricing: Mistral Large 2407 at $2.00/M vs Qwen2.5 72B Instruct at $0.12/M.
Mistral Large 2407 has a larger context window of 131,072 tokens compared to Qwen2.5 72B Instruct's 32,768 tokens. A larger context window means the model can process longer documents and conversations.