Qwen3-235B-A22B-Instruct-2507 vs Qwen3.5-4B Comparison

Comparing Qwen3-235B-A22B-Instruct-2507 and Qwen3.5-4B across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

10 benchmarks

Qwen3-235B-A22B-Instruct-2507 outperforms in 6 benchmarks (GPQA, Include, MMLU-Pro, MMLU-ProX, MMLU-Redux, SuperGPQA), while Qwen3.5-4B is better at 4 benchmarks (HMMT25, IFEval, LiveCodeBench v6, PolyMATH).

Qwen3-235B-A22B-Instruct-2507 has a slight edge in benchmark performance.

Sun Mar 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers

Sun Mar 15 2026 • llm-stats.com

Qwen3-235B-A22B-Instruct-2507

Input tokens$0.15

Output tokens$0.80

Best providerFireworks

Qwen3.5-4B

Input tokens$0.00

Output tokens$0.00

Best providerUnknown Organization

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

231.0B diff

Qwen3-235B-A22B-Instruct-2507 has 231.0B more parameters than Qwen3.5-4B, making it 5775.0% larger.

Qwen3-235B-A22B-Instruct-2507

235.0Bparameters

Qwen3.5-4B

4.0Bparameters

235.0B

Qwen3-235B-A22B-Instruct-2507

4.0B

Qwen3.5-4B

Context Window

Maximum input and output token capacity

Only Qwen3-235B-A22B-Instruct-2507 specifies input context (262,144 tokens). Only Qwen3-235B-A22B-Instruct-2507 specifies output context (131,072 tokens).

Qwen3-235B-A22B-Instruct-2507

Input262,144 tokens

Output131,072 tokens

Qwen3.5-4B

Input- tokens

Output- tokens

Sun Mar 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.5-4B supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 does not.

Qwen3.5-4B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen3-235B-A22B-Instruct-2507

Text

Images

Audio

Video

Qwen3.5-4B

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.

Qwen3-235B-A22B-Instruct-2507

Apache 2.0

Open weights

Qwen3.5-4B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22, while Qwen3.5-4B was released on 2026-03-02.

Qwen3.5-4B is 7 months newer than Qwen3-235B-A22B-Instruct-2507.

Qwen3-235B-A22B-Instruct-2507

Jul 22, 2025

7 months ago

Qwen3.5-4B

Mar 2, 2026

1 weeks ago

7mo newer

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

Outputs Comparison

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Key Takeaways

Qwen3-235B-A22B-Instruct-2507

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Higher GPQA score (77.5% vs 76.2%)

Higher Include score (79.5% vs 71.0%)

Higher MMLU-Pro score (83.0% vs 79.1%)

Higher MMLU-ProX score (79.4% vs 71.5%)

Higher MMLU-Redux score (93.1% vs 88.8%)

Higher SuperGPQA score (62.6% vs 52.9%)

Qwen3.5-4B

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Higher HMMT25 score (76.8% vs 55.4%)

Higher IFEval score (89.8% vs 88.7%)

Higher LiveCodeBench v6 score (55.8% vs 51.8%)

Higher PolyMATH score (51.1% vs 50.2%)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen3-235B-A22B-Instruct-2507	Qwen3.5-4B