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Alibaba

Qwen3 Coder Next

  • Released
  • Open weights
  • Apache 2.0
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Intelligence Index

Standard setting

9.2#74 of 83measured models
Measured · Artificial Analysis v4.3.2
API price from
$0.12 in / $0.80 out / 1M tokOpenRouter · Parasail · BF16
Context window
256K tok

Performance profile

ReasoningRank #71 of 107
73.7%
GPQA Diamond
CodingRank #76 of 94
36.2%
SciCode
Cost efficiencyRank #58 of 67
19.7pts / $
Intelligence ÷ eval cost

Capabilities

Context window

Modalities

Measured setting

Integration

Reasoning trade-offs

Source (opens in a new tab)
Intelligence Index v4.3.2
Qwen3 Coder Next
Cost per task (USD) · log scale
Qwen3 Coder NextStandard
Intelligence Index
9.2
Cost / task
$0.4697
Evaluation time
3.6m

Pricing & access

API route
OpenRouter · Parasail · BF16API
Tier & contextStandard

USD per 1M tokens

Input
$0.12
Output
$0.80
Cached input
$0.07

Run locally

GGUF
Context
Estimated memory

~49.9 GiB

RAM + VRAM

Estimate assumptions

One text chat at the selected context, with a 16-bit cache and app allowance. Operating system and image processing are extra; optimized caches can use less.

64 GiB RAMwithout GPU offload

Memory budgetOne text chat
Weights
45.2 GiB
Context + state
0.3 GiB16-bit cache
App allowance
4.5 GiB
Download
48.5 GB
Disk space
~59 GB
Available software
Ollama (opens in a new tab)LM Studio (opens in a new tab)Jan (opens in a new tab)KoboldCpp (opens in a new tab)llama.cpp (opens in a new tab)
Get GGUF (opens in a new tab)
Set up in Ollama · Q4_K_M · 8K context

Save the following as Modelfile. The context matches the estimate above.

FROM hf.co/lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
PARAMETER num_ctx 8192

Then run these commands in your terminal:

ollama create llmcurrent-local -f Modelfile
ollama run llmcurrent-local

Ollama import instructions (opens in a new tab) · For LM Studio or Jan, open the app’s model search, enter lmstudio-community/Qwen3-Coder-Next-GGUF, choose Q4_K_M, and set context to 8,192 tokens in the app’s model settings. Software links above open each app’s setup guide.

vLLM setup (opens in a new tab)

Benchmarks

Full leaderboardSource (opens in a new tab)

Humanity’s Last Exam

Frontier reasoning

Qwen3 Coder Next · Standard#91 of 126

  1. 1Claude Opus 5.5Max61.4%
  2. 2Claude Fable 5.1Max59.1%
  3. 3Gemini 4 ArgonHigh57.1%
  5. 88gpt-oss-20bHigh11.0%
  6. 89Hermes 4 - Llama-3.1 405BReasoning10.9%
  7. 90Nemotron 3.5 LightningReasoning10.6%
  8. 91Qwen3 Coder NextStandard10.1%
  9. 92Qwen3.5 4BReasoning9.9%
  10. 93Mistral Small 4Reasoning9.9%
  11. 94Granite 4.2 8BReasoning9.7%

Peers use their best measured result on this benchmark.

Alternatives

Each model at its highest-scoring measured setting · Intelligence Index v4.3.2 and its evaluation cost per task.