| Signal | Mistral Small 3.2 24B | Delta | Qwen3 Next 80B A3B Thinking |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 0 | -1 | |
Context window size | 81 | -- | |
Recency | 84 | -15 | |
Output Capacity | 85 | +10 | |
| Overall Result | 1 wins | of 5 | 2 wins |
5
days ranked higher
2
days
23
days ranked higher
Mistral AI
Alibaba
Mistral Small 3.2 24B saves you $33.75/month
That's $405.00/year compared to Qwen3 Next 80B A3B Thinking at your current usage level of 100K calls/month.
| Metric | Mistral Small 3.2 24B | Qwen3 Next 80B A3B Thinking | Winner |
|---|---|---|---|
| Overall Score | 76 | 77 | Qwen3 Next 80B A3B Thinking |
| Rank | #90 | #89 | Qwen3 Next 80B A3B Thinking |
| Quality Rank | #90 | #89 | Qwen3 Next 80B A3B Thinking |
| Adoption Rank | #90 | #89 | Qwen3 Next 80B A3B Thinking |
| Parameters | 24B | 80B | -- |
| Context Window | 131K | 131K | -- |
| Pricing | $0.06/$0.18/M | $0.10/$0.78/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Mistral Small 3.2 24B |
| Pricing | 0 | 1 | Qwen3 Next 80B A3B Thinking |
| Context window size | 81 | 81 | Mistral Small 3.2 24B |
| Recency | 84 | 99 | Qwen3 Next 80B A3B Thinking |
| Output Capacity | 85 | 75 | Mistral Small 3.2 24B |
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 76/100 (rank #90), placing it in the top 69% of all 290 models tracked.
Scores 77/100 (rank #89), placing it in the top 70% of all 290 models tracked.
With only a 2-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 73% better value per quality point. At 1M tokens/day, you'd spend $3.60/month with Mistral Small 3.2 24B vs $13.16/month with Qwen3 Next 80B A3B Thinking — a $9.56 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 ($0.18/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (77/100) 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 Qwen3 Next 80B A3B Thinking are extremely close in overall performance (only 1.5 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
73% 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 | Qwen3 Next 80B A3B Thinking |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Mistral Small 3.2 24B saves you $0.7875/month
That's 71% cheaper than Qwen3 Next 80B A3B Thinking 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 | Qwen3 Next 80B A3B Thinking |
|---|---|---|
| Context Window | 131K | 131K |
| Max Output Tokens | 131,072 | 32,768 |
| Open Source | Yes | Yes |
| Created | Jun 20, 2025 | Sep 11, 2025 |
Qwen3 Next 80B A3B Thinking scores 77/100 (rank #89) compared to Mistral Small 3.2 24B's 76/100 (rank #90), giving it a 2-point advantage. Qwen3 Next 80B A3B Thinking 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 #90 and Qwen3 Next 80B A3B Thinking is ranked #89 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 $0.18/M output tokens vs Qwen3 Next 80B A3B Thinking's $0.78/M output tokens — 4.3x more expensive. Input token pricing: Mistral Small 3.2 24B at $0.06/M vs Qwen3 Next 80B A3B Thinking at $0.10/M.
Mistral Small 3.2 24B has a larger context window of 131,072 tokens compared to Qwen3 Next 80B A3B Thinking's 131,072 tokens. A larger context window means the model can process longer documents and conversations.