Model Comparison

DeepSeek-V3.2-Exp vs Qwen3-235B-A22B-Instruct-2507

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp is 1.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek-V3.2-Exp outperforms in 5 benchmarks (Aider-Polyglot, AIME 2025, GPQA, MMLU-Pro, SimpleQA), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks.

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.

Sun May 10 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2-Exp costs less

For input processing, DeepSeek-V3.2-Exp ($0.27/1M tokens) is 1.8x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.15/1M tokens).

For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 2.0x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).

In conclusion, Qwen3-235B-A22B-Instruct-2507 is more expensive than DeepSeek-V3.2-Exp.*

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

Lowest available price from all providers

Sun May 10 2026 • llm-stats.com

DeepSeek-V3.2-Exp

Input tokens$0.27

Output tokens$0.41

Best providerNovita

Qwen3-235B-A22B-Instruct-2507

Input tokens$0.15

Output tokens$0.80

Best providerFireworks

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

450.0B diff

DeepSeek-V3.2-Exp has 450.0B more parameters than Qwen3-235B-A22B-Instruct-2507, making it 191.5% larger.

DeepSeek-V3.2-Exp

685.0Bparameters

Qwen3-235B-A22B-Instruct-2507

235.0Bparameters

685.0B

DeepSeek-V3.2-Exp

235.0B

Qwen3-235B-A22B-Instruct-2507

Context Window

Maximum input and output token capacity

Qwen3-235B-A22B-Instruct-2507 accepts 262,144 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. Qwen3-235B-A22B-Instruct-2507 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.

DeepSeek-V3.2-Exp

Input163,840 tokens

Output65,536 tokens

Qwen3-235B-A22B-Instruct-2507

Input262,144 tokens

Output131,072 tokens

Sun May 10 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2-Exp is licensed under MIT, while Qwen3-235B-A22B-Instruct-2507 uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3.2-Exp

MIT

Open weights

Qwen3-235B-A22B-Instruct-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Exp was released on 2025-09-29, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.

DeepSeek-V3.2-Exp is 2 months newer than Qwen3-235B-A22B-Instruct-2507.

DeepSeek-V3.2-Exp

Sep 29, 2025

7 months ago

2mo newer

Qwen3-235B-A22B-Instruct-2507

Jul 22, 2025

9 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

DeepSeek-V3.2-Exp is available from Novita. Qwen3-235B-A22B-Instruct-2507 is available from Fireworks, Novita.

DeepSeek-V3.2-Exp

Novita

Input Price:Input: $0.27/1MOutput Price:Output: $0.41/1M

Qwen3-235B-A22B-Instruct-2507

Fireworks

Input Price:Input: $0.15/1MOutput Price:Output: $0.80/1M

Novita

Input Price:Input: $0.15/1MOutput Price:Output: $0.80/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek-V3.2-Exp

View details

DeepSeek

Less expensive output tokens

Higher Aider-Polyglot score (74.5% vs 57.3%)

Higher AIME 2025 score (89.3% vs 70.3%)

Higher GPQA score (79.9% vs 77.5%)

Higher MMLU-Pro score (85.0% vs 83.0%)

Higher SimpleQA score (97.1% vs 54.3%)

Qwen3-235B-A22B-Instruct-2507

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Less expensive input tokens

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V3.2-Exp	Qwen3-235B-A22B-Instruct-2507

FAQ

Common questions about DeepSeek-V3.2-Exp vs Qwen3-235B-A22B-Instruct-2507.

Which is better, DeepSeek-V3.2-Exp or Qwen3-235B-A22B-Instruct-2507?

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp is made by DeepSeek and Qwen3-235B-A22B-Instruct-2507 is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.2-Exp compare to Qwen3-235B-A22B-Instruct-2507 in benchmarks?

DeepSeek-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%. Qwen3-235B-A22B-Instruct-2507 scores ZebraLogic: 95.0%, MMLU-Redux: 93.1%, IFEval: 88.7%, MultiPL-E: 87.9%, Creative Writing v3: 87.5%.

Is DeepSeek-V3.2-Exp cheaper than Qwen3-235B-A22B-Instruct-2507?

Qwen3-235B-A22B-Instruct-2507 is 1.8x cheaper for input tokens. DeepSeek-V3.2-Exp costs $0.27/M input and $0.41/M output via novita. Qwen3-235B-A22B-Instruct-2507 costs $0.15/M input and $0.80/M output via fireworks.

What are the context window sizes for DeepSeek-V3.2-Exp and Qwen3-235B-A22B-Instruct-2507?

DeepSeek-V3.2-Exp supports 164K tokens and Qwen3-235B-A22B-Instruct-2507 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2-Exp and Qwen3-235B-A22B-Instruct-2507?

Key differences include context window (164K vs 262K), input pricing ($0.27 vs $0.15/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2-Exp and Qwen3-235B-A22B-Instruct-2507?

DeepSeek-V3.2-Exp is developed by DeepSeek and Qwen3-235B-A22B-Instruct-2507 is developed by Alibaba Cloud / Qwen Team.