Qwen2.5 Coder 32B Instruct
Qwen · released Nov 11, 2024
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...
Specification
- Context window
- 33K
- Max output
- 33K
- Knowledge cutoff
- Jun 30, 2024
- Parameters
- 32.8B
- Licence
- apache-2.0
- Serving providers
- 1
- Moderated
- No
- Uptime
- 100.0%
Intelligence
6.9
23th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
26 t/s
Median across providers
Latency
484ms
Time to first token
Cost per task
$0.06
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro63.5%
- GPQA Diamond41.7%
- LiveCodeBench29.5%
- SciCode27.1%
- AIME 202512.0%
- Humanity's Last Exam3.5%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 63.5% |
| GPQA Diamond | 41.7% |
| LiveCodeBench | 29.5% |
| SciCode | 27.1% |
| AIME 2025 | 12.0% |
| Humanity's Last Exam | 3.5% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Nemotron 3 Nano 30B A3BNVIDIA7.2
- Mistral Large 2407Mistral AI7.0
- Qwen2.5 Coder 32B InstructQwen6.9
- GLM 4.5VZ.ai6.8
- GPT-4OpenAI6.8
- Gemini 2.5 Flash LiteGoogle6.7
- GPT-4o-miniOpenAI6.7
- GPT-4o-mini (2024-07-18)OpenAI6.7
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Nemotron 3 Nano 30B A3B | 7.2 |
| Mistral Large 2407 | 7.0 |
| Qwen2.5 Coder 32B Instruct | 6.9 |
| GLM 4.5V | 6.8 |
| GPT-4 | 6.8 |
| Gemini 2.5 Flash Lite | 6.7 |
| GPT-4o-mini | 6.7 |
| GPT-4o-mini (2024-07-18) | 6.7 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.66
- Output / 1M tokens
- $1
- Cached input / 1M
- Not offered
- Blended 3:1
- $0.745
One task, estimated
$0.06
- Input tokens
- 50,000
- Output tokens
- 25,000
- Profile
- Standard
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.