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Qwen3.5 397B-A17B on Run BiOS

qwen3.5-397b-a17b

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Top up $20 and we add 50% — that’s about 20.0M input tokens extra on Qwen3.5 397B-A17B, on us.

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256K contextVisionTool callingReasoningPrompt caching

Pricing

USD per 1M tokens
Input$0.50
Output$3.40
Cached input$0.0590% below fresh input

Billed per token with no minimum and no monthly fee. Price your workload

Capabilities and limits

Context window
256K tokens
Modality
text
Tool calling
Supported
Vision
Supported
Reasoning
Optional
Reasoning effort
low, medium, high
Prompt caching
Supported (automatic)

Measured performance

7d
Time to first token (p50)
400 ms
Time to first token (p95)
3.0 s
Throughput (avg)
48.2 tok/s
Throughput (p50)
100.0 tok/s

Measured on real Run BiOS traffic over the trailing 7d. Your figures will vary with prompt shape and region.

Call it now — OpenAI-compatible
curl https://api.runbios.ai/v1/chat/completions \
  -H "Authorization: Bearer $BIOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-397b-a17b",
    "messages": [{ "role": "user", "content": "Hello" }]
  }'

Existing OpenAI SDK code works by changing the base URL and the key. API overview

Model card

Curated by Run BiOS · benchmark figures, where present, are the card’s own, not our measurements

Qwen3.5-397B-A17B is the first open-weight model in the Qwen3.5 series from the Qwen team. A native vision-language model, it demonstrates outstanding results across a full range of benchmark evaluations, including reasoning, coding, agent capabilities, and multimodal understanding. Built on an innovative hybrid architecture that fuses linear attention (via Gated Delta Networks) with a sparse mixture-of-experts, the model attains remarkable inference efficiency: although it comprises 397 billion total parameters, just 17 billion are activated per forward pass, optimizing both speed and cost without sacrificing capability. Language and dialect support expanded from 119 to 201, providing broader accessibility and enhanced support to users around the world.

Qwen3.5 Highlights

  • Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
  • Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
  • Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
  • Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
  • Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

Model Overview

  • Type: Causal Language Model with Vision Encoder
  • Training Stage: Pre-training & Post-training
  • Language Model
    • Number of Parameters: 397B in total and 17B activated
    • Hidden Dimension: 4096
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 60
      • Hidden Layout: 15 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
    • Gated DeltaNet:
      • Number of Linear Attention Heads: 64 for V and 16 for QK
      • Head Dimension: 128
    • Gated Attention:
      • Number of Attention Heads: 32 for Q and 2 for KV
      • Head Dimension: 256
      • Rotary Position Embedding Dimension: 64
    • Mixture Of Experts
      • Number of Experts: 512
      • Number of Activated Experts: 10 Routed + 1 Shared
      • Expert Intermediate Dimension: 1024
    • LM Output: 248320 (Padded)
    • MTP: trained with multi-steps
  • Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

The model operates as a hybrid reasoner: thinking mode can be enabled or disabled per request.

Benchmark Results

Language

GPT5.2 Claude 4.5 Opus Gemini-3 Pro Qwen3-Max-Thinking K2.5-1T-A32B Qwen3.5-397B-A17B
Knowledge
MMLU-Pro 87.4 89.5 89.8 85.7 87.1 87.8
MMLU-Redux 95.0 95.6 95.9 92.8 94.5 94.9
SuperGPQA 67.9 70.6 74.0 67.3 69.2 70.4
C-Eval 90.5 92.2 93.4 93.7 94.0 93.0
Instruction Following
IFEval 94.8 90.9 93.5 93.4 93.9 92.6
IFBench 75.4 58.0 70.4 70.9 70.2 76.5
MultiChallenge 57.9 54.2 64.2 63.3 62.7 67.6
Long Context
AA-LCR 72.7 74.0 70.7 68.7 70.0 68.7
LongBench v2 54.5 64.4 68.2 60.6 61.0 63.2
STEM
GPQA 92.4 87.0 91.9 87.4 87.6 88.4
HLE 35.5 30.8 37.5 30.2 30.1 28.7
HLE-Verified¹ 43.3 38.8 48 37.6 -- 37.6
Reasoning
LiveCodeBench v6 87.7 84.8 90.7 85.9 85.0 83.6
HMMT Feb 25 99.4 92.9 97.3 98.0 95.4 94.8
HMMT Nov 25 100 93.3 93.3 94.7 91.1 92.7
IMOAnswerBench 86.3 84.0 83.3 83.9 81.8 80.9
AIME26 96.7 93.3 90.6 93.3 93.3 91.3
General Agent
BFCL-V4 63.1 77.5 72.5 67.7 68.3 72.9
TAU2-Bench 87.1 91.6 85.4 84.6 77.0 86.7
VITA-Bench 38.2 56.3 51.6 40.9 41.9 49.7
DeepPlanning 44.6 33.9 23.3 28.7 14.5 34.3
Tool Decathlon 43.8 43.5 36.4 18.8 27.8 38.3
MCP-Mark 57.5 42.3 53.9 33.5 29.5 46.1
Search Agent³
HLE w/ tool 45.5 43.4 45.8 49.8 50.2 48.3
BrowseComp 65.8 67.8 59.2 53.9 --/74.9 69.0/78.6
BrowseComp-zh 76.1 62.4 66.8 60.9 -- 70.3
WideSearch 76.8 76.4 68.0 57.9 72.7 74.0
Seal-0 45.0 47.7 45.5 46.9 57.4 46.9
Multilingualism
MMMLU 89.5 90.1 90.6 84.4 86.0 88.5
MMLU-ProX 83.7 85.7 87.7 78.5 82.3 84.7
NOVA-63 54.6 56.7 56.7 54.2 56.0 59.1
INCLUDE 87.5 86.2 90.5 82.3 83.3 85.6
Global PIQA 90.9 91.6 93.2 86.0 89.3 89.8
PolyMATH 62.5 79.0 81.6 64.7 43.1 73.3
WMT24++ 78.8 79.7 80.7 77.6 77.6 78.9
MAXIFE 88.4 79.2 87.5 84.0 72.8 88.2
Coding Agent
SWE-bench Verified 80.0 80.9 76.2 75.3 76.8 76.4
SWE-bench Multilingual 72.0 77.5 65.0 66.7 73.0 69.3
SecCodeBench 68.7 68.6 62.4 57.5 61.3 68.3
Terminal Bench 2 54.0 59.3 54.2 22.5 50.8 52.5

Vision Language

GPT5.2 Claude 4.5 Opus Gemini-3 Pro Qwen3-VL-235B-A22B K2.5-1T-A32B Qwen3.5-397B-A17B
STEM and Puzzle
MMMU 86.7 80.7 87.2 80.6 84.3 85.0
MMMU-Pro 79.5 70.6 81.0 69.3 78.5 79.0
MathVision 83.0 74.3 86.6 74.6 84.2 88.6
Mathvista(mini) 83.1 80.0 87.9 85.8 90.1 90.3
We-Math 79.0 70.0 86.9 74.8 84.7 87.9
DynaMath 86.8 79.7 85.1 82.8 84.4 86.3
ZEROBench 9 3 10 4 9 12
ZEROBench_sub 33.2 28.4 39.0 28.4 33.5 41.0
BabyVision 34.4 14.2 49.7 22.2 36.5 52.3/43.3
General VQA
RealWorldQA 83.3 77.0 83.3 81.3 81.0 83.9
MMStar 77.1 73.2 83.1 78.7 80.5 83.8
HallusionBench 65.2 64.1 68.6 66.7 69.8 71.4
MMBenchEN-DEV-v1.1 88.2 89.2 93.7 89.7 94.2 93.7
SimpleVQA 55.8 65.7 73.2 61.3 71.2 67.1
Text Recognition and Document Understanding
OmniDocBench1.5 85.7 87.7 88.5 84.5 88.8 90.8
CharXiv(RQ) 82.1 68.5 81.4 66.1 77.5 80.8
MMLongBench-Doc -- 61.9 60.5 56.2 58.5 61.5
CC-OCR 70.3 76.9 79.0 81.5 79.7 82.0
AI2D_TEST 92.2 87.7 94.1 89.2 90.8 93.9
OCRBench 80.7 85.8 90.4 87.5 92.3 93.1
Spatial Intelligence
ERQA 59.8 46.8 70.5 52.5 -- 67.5
CountBench 91.9 90.6 97.3 93.7 94.1 97.2
RefCOCO(avg) -- -- 84.1 91.1 87.8 92.3
ODInW13 -- -- 46.3 43.2 -- 47.0
EmbSpatialBench 81.3 75.7 61.2 84.3 77.4 84.5
RefSpatialBench -- -- 65.5 69.9 -- 73.6
LingoQA 68.8 78.8 72.8 66.8 68.2 81.6
V* 75.9 67.0 88.0 85.9 77.0 95.8/91.1
Hypersim -- -- -- 11.0 -- 12.5
SUNRGBD -- -- -- 34.9 -- 38.3
Nuscene -- -- -- 13.9 -- 16.0
Video Understanding
VideoMME(w sub.) 86 77.6 88.4 83.8 87.4 87.5
VideoMME(w/o sub.) 85.8 81.4 87.7 79.0 83.2 83.7
VideoMMMU 85.9 84.4 87.6 80.0 86.6 84.7
MLVU (M-Avg) 85.6 81.7 83.0 83.8 85.0 86.7
MVBench 78.1 67.2 74.1 75.2 73.5 77.6
LVBench 73.7 57.3 76.2 63.6 75.9 75.5
MMVU 80.8 77.3 77.5 71.1 80.4 75.4
Visual Agent
ScreenSpot Pro -- 45.7 72.7 62.0 -- 65.6
OSWorld-Verified 38.2 66.3 -- 38.1 63.3 62.2
AndroidWorld -- -- -- 63.7 -- 66.8
Medical VQA
SLAKE 76.9 76.4 81.3 72.5 81.6 79.9
PMC-VQA 58.9 59.9 62.3 56.1 63.3 64.2
MedXpertQA-MM 73.3 63.6 76.0 47.6 65.3 70.0

Evaluation notes (as published by the Qwen team)

  • HLE-Verified: a verified and revised version of Humanity's Last Exam (HLE), accompanied by a transparent, component-wise verification protocol and a fine-grained error taxonomy, open-sourced by its authors.
  • TAU2-Bench: the official setup, except for the airline domain, where all models are evaluated applying the fixes proposed in the Claude Opus 4.5 system card.
  • MCPMark: the code-hosting MCP server is pinned to a fixed version; browser-automation tool responses are truncated at 32k tokens.
  • Search Agent: most search agents built on this model adopt a simple context-folding strategy (256k): once the cumulative tool-response length reaches a preset threshold, earlier tool responses are pruned from the history to keep the context within limits.
  • BrowseComp: two strategies were tested — simple context-folding achieved 69.0, while the same discard-all strategy as DeepSeek-V3.2 and Kimi K2.5 achieved 78.6.
  • WideSearch: a 256k context window without any context management.
  • MMLU-ProX: averaged accuracy on 29 languages.
  • WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; averaged scores on 55 languages using XCOMET-XXL.
  • MAXIFE: accuracy on English + multilingual original prompts (23 settings in total).
  • MathVision: this model's score is evaluated using a fixed prompt ("Please reason step by step, and put your final answer within \boxed{}."); for other models, the higher score between runs with and without the \boxed{} formatting is reported.
  • BabyVision / V*: this model's score is reported with CI (Code Interpreter) enabled; without CI, the results are 43.3 and 91.1 respectively.
  • Empty cells (--) indicate scores not yet available or not applicable.

On Run BiOS

On the Run BiOS platform, Qwen3.5-397B-A17B is available as the serverless model qwen3.5-397b-a17b with a 262,144-token context window, function calling, hybrid reasoning (thinking mode can be enabled or disabled per request), vision input, and prompt caching for repeated prefixes.

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