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Alibaba

Qwen3.8-27B

  • Released
  • Open weights
  • Apache 2.0
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Intelligence Index

Extra high setting

33.7#31 of 83measured models
Measured · Artificial Analysis v4.3.2
API price from
≈ $0.43 in / $2.55 out / 1M tokOpenRouter · Alibaba
Context window
262.14K tok

Performance profile

ReasoningRank #18 of 107
90.5%
GPQA Diamond
CodingRank #44 of 94
46.6%
SciCode
Cost efficiencyRank #49 of 67
33.4pts / $
Intelligence ÷ eval cost

Capabilities

Context window

Modalities

Reasoning modes

Avg reasoning tokens / task — Artificial Analysis Intelligence Index v4.3.2: weighted average reasoning tokens per benchmark task, not a token limit. Source (opens in a new tab)

Integration

Reasoning trade-offs

Source (opens in a new tab)
Intelligence Index v4.3.2
Qwen3.8-27B
Cost per task (USD) · log scale
Qwen3.8-27BX-High
Intelligence Index
33.7
Cost / task
$1.01
Evaluation time
21.1m

Pricing & access

API route
OpenRouter · AlibabaAPI
Tier & contextStandard

USD per 1M tokens

Input
$0.425
Output
$2.55
Cached input
$0.085

Run locally

GGUF
Context
Estimated memory

~18.3 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.

24 GiB RAMwithout GPU offload

Memory budgetOne text chat
Weights
15.7 GiB
Context + state
0.6 GiB16-bit cache
App allowance
2 GiB
Disk space
~25 GBIncludes image file
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/Qwen3.8-27B-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/Qwen3.8-27B-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.

Images need extra memoryvLLM setup (opens in a new tab)

Benchmarks

Full leaderboardSource (opens in a new tab)

Humanity’s Last Exam

Frontier reasoning

Qwen3.8-27B · Extra high#41 of 126

  1. 1Claude Opus 5.5Max61.4%
  2. 2Claude Fable 5.1Max59.1%
  3. 3Gemini 4 ArgonHigh57.1%
  5. 38Beam 1.0Reasoning34.7%
  6. 39DeepSeek V4 Flash VisionMax34.5%
  7. 40Apodex 1.1Reasoning34.1%
  8. 41Qwen3.8-27BExtra high33.9%
  9. 42Nex-N2-ProReasoning33.7%
  10. 43LongCat 2.0Reasoning33.7%
  11. 44Agnes 2.5 Pro AlphaReasoning33.6%

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.