| Signal | Mistral Small 3.2 24B | Delta | Qwen VL Max |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 0 | -2 | |
Context window size | 81 | 0 | |
Recency | 81 | +25 | |
Output Capacity | 20 | -55 | |
| Overall Result | 1 wins | of 5 | 3 wins |
6
days higher
2
days
22
days higher
Mistral AI
Alibaba
Mistral Small 3.2 24B saves you $138.50/month
That's $1662.00/year compared to Qwen VL Max at your current usage level of 100K calls/month.
| Metric | Mistral Small 3.2 24B | Qwen VL Max | Winner |
|---|---|---|---|
| Overall Score | 67 | 68 | Qwen VL Max |
| Rank | #181 | #179 | Qwen VL Max |
| Quality Rank | #181 | #179 | Qwen VL Max |
| Adoption Rank | #181 | #179 | Qwen VL Max |
| Parameters | 24B | -- | -- |
| Context Window | 128K | 131K | Qwen VL Max |
| Pricing | $0.07/$0.20/M | $0.52/$2.08/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Mistral Small 3.2 24B |
| Pricing | 0 | 2 | Qwen VL Max |
| Context window size | 81 | 81 | Qwen VL Max |
| Recency | 81 | 56 | Mistral Small 3.2 24B |
| Output Capacity | 20 | 75 | Qwen VL Max |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 67/100 (rank #181), placing it in the top 38% of all 290 models tracked.
Scores 68/100 (rank #179), placing it in the top 39% 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.
Mistral Small 3.2 24B offers 89% better value per quality point. At 1M tokens/day, you'd spend $4.13/month with Mistral Small 3.2 24B vs $39.00/month with Qwen VL Max - a $34.88 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. Mistral Small 3.2 24B 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 ($0.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (68/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Mistral Small 3.2 24B and Qwen VL Max are extremely close in overall performance (only 0.7999999999999972 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Mistral Small 3.2 24B
Marginally better benchmark scores; both are excellent
Best for Cost
Mistral Small 3.2 24B
89% lower pricing; better value at scale
Best for Reliability
Mistral Small 3.2 24B
Higher uptime and faster response speeds
Best for Prototyping
Mistral Small 3.2 24B
Stronger community support and better developer experience
Best for Production
Mistral Small 3.2 24B
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Mistral Small 3.2 24B | Qwen VL Max |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Mistral Small 3.2 24B saves you $3.06/month
That's 89% cheaper than Qwen VL Max 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 Small 3.2 24B | Qwen VL Max |
|---|---|---|
| Context Window | 128K | 131K |
| Max Output Tokens | -- | 32,768 |
| Open Source | Yes | No |
| Created | Jun 20, 2025 | Feb 1, 2025 |
Qwen VL Max scores 68/100 (rank #179) compared to Mistral Small 3.2 24B's 67/100 (rank #181), giving it a 1-point advantage. Qwen VL Max is the stronger overall choice, though Mistral Small 3.2 24B may excel in specific areas like cost efficiency.
Mistral Small 3.2 24B is ranked #181 and Qwen VL Max is ranked #179 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Mistral Small 3.2 24B is cheaper at $0.20/M output tokens vs Qwen VL Max's $2.08/M output tokens - 10.4x more expensive. Input token pricing: Mistral Small 3.2 24B at $0.07/M vs Qwen VL Max at $0.52/M.
Qwen VL Max has a larger context window of 131,072 tokens compared to Mistral Small 3.2 24B's 128,000 tokens. A larger context window means the model can process longer documents and conversations.