Model Comparison

Qwen3.5-397B-A17B vs Qwen3 VL 4B Thinking

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Qwen3 VL 4B Thinking is 4.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

10 benchmarks

Qwen3.5-397B-A17B outperforms in 10 benchmarks (GPQA, HMMT25, IFEval, Include, LiveCodeBench v6, MMLU-Pro, MMLU-ProX, MMLU-Redux, PolyMATH, SuperGPQA), while Qwen3 VL 4B Thinking is better at 0 benchmarks.

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

Tue Apr 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3 VL 4B Thinking costs less

For input processing, Qwen3.5-397B-A17B ($0.60/1M tokens) is 6.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).

For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 3.6x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).

In conclusion, Qwen3.5-397B-A17B is more expensive than Qwen3 VL 4B Thinking.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers

Tue Apr 14 2026 • llm-stats.com

Qwen3.5-397B-A17B

Input tokens$0.60

Output tokens$3.60

Best providerNovita

Qwen3 VL 4B Thinking

Input tokens$0.10

Output tokens$1.00

Best providerDeepinfra

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

393.0B diff

Qwen3.5-397B-A17B has 393.0B more parameters than Qwen3 VL 4B Thinking, making it 9825.0% larger.

Qwen3.5-397B-A17B

397.0Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

397.0B

Qwen3.5-397B-A17B

4.0B

Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Both models have the same input context window of 262,144 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.

Qwen3.5-397B-A17B

Input262,144 tokens

Output64,000 tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Tue Apr 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.5-397B-A17B and Qwen3 VL 4B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3.5-397B-A17B

Text

Images

Audio

Video

Qwen3 VL 4B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Qwen3.5-397B-A17B

Apache 2.0

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3.5-397B-A17B is 5 months newer than Qwen3 VL 4B Thinking.

Qwen3.5-397B-A17B

Feb 16, 2026

1 months ago

4mo newer

Qwen3 VL 4B Thinking

Sep 22, 2025

6 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Qwen3.5-397B-A17B is available from Novita. Qwen3 VL 4B Thinking is available from DeepInfra.

Qwen3.5-397B-A17B

Novita

Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

Qwen3 VL 4B Thinking

Deepinfra

Input Price:Input: $0.10/1MOutput Price:Output: $1.00/1M

* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Higher GPQA score (88.4% vs 64.1%)

Higher HMMT25 score (92.7% vs 53.1%)

Higher IFEval score (92.6% vs 82.6%)

Higher Include score (85.6% vs 64.6%)

Higher LiveCodeBench v6 score (83.6% vs 51.3%)

Higher MMLU-Pro score (87.8% vs 73.6%)

Higher MMLU-ProX score (84.7% vs 65.0%)

Higher MMLU-Redux score (94.9% vs 86.0%)

Higher PolyMATH score (73.3% vs 44.6%)

Higher SuperGPQA score (70.4% vs 46.8%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Less expensive input tokens

Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature	Qwen3.5-397B-A17B	Qwen3 VL 4B Thinking

FAQ

Common questions about Qwen3.5-397B-A17B vs Qwen3 VL 4B Thinking

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Qwen3.5-397B-A17B is made by Alibaba Cloud / Qwen Team and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

Qwen3.5-397B-A17B scores MMLU-Redux: 94.9%, HMMT 2025: 94.8%, C-Eval: 93.0%, HMMT25: 92.7%, IFEval: 92.6%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

Qwen3 VL 4B Thinking is 6.0x cheaper for input tokens. Qwen3.5-397B-A17B costs $0.60/M input and $3.60/M output via novita. Qwen3 VL 4B Thinking costs $0.10/M input and $1.00/M output via deepinfra.

Qwen3.5-397B-A17B supports 262K tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Key differences include input pricing ($0.60 vs $0.10/M). See the full comparison above for benchmark-by-benchmark results.

Qwen3.5-397B-A17B vs Qwen3 VL 4B Thinking

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

Input Capabilities

Qwen3.5-397B-A17B

Qwen3 VL 4B Thinking

License

Release Timeline

Knowledge Cutoff

Provider Availability

Qwen3.5-397B-A17B

Qwen3 VL 4B Thinking

Outputs Comparison

Key Takeaways

Qwen3.5-397B-A17B

Qwen3 VL 4B Thinking

Detailed Comparison

FAQ

Which is better, Qwen3.5-397B-A17B or Qwen3 VL 4B Thinking?

How does Qwen3.5-397B-A17B compare to Qwen3 VL 4B Thinking in benchmarks?

Is Qwen3.5-397B-A17B cheaper than Qwen3 VL 4B Thinking?

What are the context window sizes for Qwen3.5-397B-A17B and Qwen3 VL 4B Thinking?

What are the main differences between Qwen3.5-397B-A17B and Qwen3 VL 4B Thinking?