Model comparison
Qwen2.5-VL vs Pixtral 12B (24.09)
- Input
- $0.80 per 1M input tokens
- Output
- $1.00 per 1M output tokens
- Context
- 32,768 tokens (extendable to ~64K for long video via YaRN; some API providers expose up to 131K)
- Released
- 2025-01-26
- Input
- $0.15 / 1M tokens
- Output
- $0.15 / 1M tokens
- Context
- 128K tokens (131,072)
- Released
- 2024-09-17
Qwen2.5-VL scores higher on 4 of the 4 benchmarks both models report; Pixtral 12B (24.09) is cheaper on input tokens ($0.2 vs $0.8 per 1M, 5.3×); Pixtral 12B (24.09) has the larger context window (128K vs 33K).
Specs and pricing side by side
| Spec | Qwen2.5-VL | Pixtral 12B (24.09) |
|---|---|---|
| Maker | Alibaba (Qwen) | Mistral AI |
| Released | 2025-01-26 | 2024-09-17 |
| Context window | 32,768 tokens (extendable to ~64K for long video via YaRN; some API providers expose up to 131K) | 128K tokens (131,072) |
| Max output | Not separately documented; bounded by the… | Not separately published by Mistral |
| Input price | $0.80 per 1M input tokens | $0.15 / 1M tokens |
| Output price | $1.00 per 1M output tokens | $0.15 / 1M tokens |
| Parameters | Four open sizes: 3B, 7B, 32B and 72B (each in base and instruction-tuned variants) | ~12B decoder + ~400M vision encoder (~12.4B total) |
| License | Mixed by size: Qwen2.5-VL-72B-Instruct under the Qwen License; 7B and 32B under Apache 2.0; 3B under the Qwen Research License (non-commercial only). | Apache 2.0 |
| Weights | Open weights | Open weightssame |
| Modalities | text, image, video | text, image |
| Knowledge cutoff | Not officially published by Alibaba | Not officially published by Mistral |
Benchmarks both models report
| Benchmark | Qwen2.5-VL | Pixtral 12B (24.09) |
|---|---|---|
| MMMU | 70.2% | 52% |
| DocVQA | 96.4% | 90.7% |
| ChartQA | 89.5% | 81.8% |
| MathVista | 74.8% | 58.3% |
Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.
Which one should you pick?
Pick Qwen2.5-VL if…
- You care about MMMU, DocVQA, ChartQA (it scores higher on 4 shared benchmarks).
- You need video input, which Pixtral 12B (24.09) lacks.
- Your use case is: document AI: extracting text, tables and layout from invoices, contracts, research papers and scanned forms.
Pick Pixtral 12B (24.09) if…
- Cost matters: input tokens cost 5.3× less ($0.2 vs $0.8 per 1M).
- You need the longer context (128K vs 33K).
- Your use case is: document and form understanding (OCR-style extraction, document QA).
FAQ
Which is better, Qwen2.5-VL or Pixtral 12B (24.09)?
Qwen2.5-VL scores higher on 4 of the 4 benchmarks both models report; Pixtral 12B (24.09) is cheaper on input tokens ($0.2 vs $0.8 per 1M, 5.3×); Pixtral 12B (24.09) has the larger context window (128K vs 33K). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is Pixtral 12B (24.09)?
Qwen2.5-VL: $0.80 per 1M input tokens input, $1.00 per 1M output tokens output. Pixtral 12B (24.09): $0.15 / 1M tokens input, $0.15 / 1M tokens output (list API prices as published by each maker).
What are the context windows of Qwen2.5-VL and Pixtral 12B (24.09)?
Qwen2.5-VL offers 32,768 tokens (extendable to ~64K for long video via YaRN; some API providers expose up to 131K) with up to Not separately documented; bounded by the 32,768-token context window output; Pixtral 12B (24.09) offers 128K tokens (131,072) with up to Not separately published by Mistral output.
Is Qwen2.5-VL newer than Pixtral 12B (24.09)?
Qwen2.5-VL was released 2025-01-26; Pixtral 12B (24.09) was released 2024-09-17.