Qwen3 Coder Next
Qwen · released Feb 4, 2026
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...
Specification
- Context window
- 262K
- Max output
- 262K
- Knowledge cutoff
- Not stated
- Parameters
- 79.7B
- Licence
- apache-2.0
- Serving providers
- 5
- Moderated
- No
- Uptime
- 100.0%
Intelligence
21.3
62th percentile
Coding
36.2
Coding Index
Agentic
8.9
Agentic Index
Output speed
129 t/s
Median across providers
Latency
567ms
Time to first token
Cost per task
$0.03
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
- τ²-bench (Telecom)79.5%
- GPQA Diamond73.7%
- AA-LCR (long context)42.3%
- IFBench35.2%
- SciCode32.3%
- Terminal-Bench Hard18.2%
- Humanity's Last Exam10.1%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| τ²-bench (Telecom) | 79.5% |
| GPQA Diamond | 73.7% |
| AA-LCR (long context) | 42.3% |
| IFBench | 35.2% |
| SciCode | 32.3% |
| Terminal-Bench Hard | 18.2% |
| Humanity's Last Exam | 10.1% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Mercury 2Inception21.9
- Qwen3.5-9BQwen21.8
- V3.1 TerminusDeepSeek21.7
- Qwen3 Coder NextQwen21.3
- R1DeepSeek20.4
- North Mini Code (free)Cohere20.2
- GPT-5 NanoOpenAI20.1
- GLM 4.5Z.ai19.7
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Mercury 2 | 21.9 |
| Qwen3.5-9B | 21.8 |
| V3.1 Terminus | 21.7 |
| Qwen3 Coder Next | 21.3 |
| R1 | 20.4 |
| North Mini Code (free) | 20.2 |
| GPT-5 Nano | 20.1 |
| GLM 4.5 | 19.7 |
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
- $0.12
- Output / 1M tokens
- $0.8
- Cached input / 1M
- $0.07
- Blended 3:1
- $0.29
One task, estimated
$0.03
- 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.