Model Comparison

Gemini 2.5 Pro Preview 06-05 vs Qwen3-235B-A22B-Instruct-2507

Gemini 2.5 Pro Preview 06-05 shows notably better performance in the majority of benchmarks. Qwen3-235B-A22B-Instruct-2507 is 11.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Gemini 2.5 Pro Preview 06-05 outperforms in 3 benchmarks (Aider-Polyglot, AIME 2025, GPQA), while Qwen3-235B-A22B-Instruct-2507 is better at 1 benchmark (SimpleQA).

Gemini 2.5 Pro Preview 06-05 shows notably better performance in the majority of benchmarks.

Wed Apr 22 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3-235B-A22B-Instruct-2507 costs less

For input processing, Gemini 2.5 Pro Preview 06-05 ($1.25/1M tokens) is 8.3x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.15/1M tokens).

For output processing, Gemini 2.5 Pro Preview 06-05 ($10.00/1M tokens) is 12.5x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).

In conclusion, Gemini 2.5 Pro Preview 06-05 is more expensive than Qwen3-235B-A22B-Instruct-2507.*

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

Lowest available price from all providers
Wed Apr 22 2026 • llm-stats.com
Google
Gemini 2.5 Pro Preview 06-05
Input tokens$1.25
Output tokens$10.00
Best providerGoogle
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507
Input tokens$0.15
Output tokens$0.80
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Gemini 2.5 Pro Preview 06-05 accepts 1,048,576 input tokens compared to Qwen3-235B-A22B-Instruct-2507's 262,144 tokens. Qwen3-235B-A22B-Instruct-2507 can generate longer responses up to 131,072 tokens, while Gemini 2.5 Pro Preview 06-05 is limited to 65,535 tokens.

Google
Gemini 2.5 Pro Preview 06-05
Input1,048,576 tokens
Output65,535 tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507
Input262,144 tokens
Output131,072 tokens
Wed Apr 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.5 Pro Preview 06-05 supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 does not.

Gemini 2.5 Pro Preview 06-05 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 2.5 Pro Preview 06-05

Text
Images
Audio
Video

Qwen3-235B-A22B-Instruct-2507

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.5 Pro Preview 06-05 is licensed under a proprietary license, 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.

Gemini 2.5 Pro Preview 06-05

Proprietary

Closed source

Qwen3-235B-A22B-Instruct-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemini 2.5 Pro Preview 06-05 was released on 2025-06-05, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.

Qwen3-235B-A22B-Instruct-2507 is 2 months newer than Gemini 2.5 Pro Preview 06-05.

Gemini 2.5 Pro Preview 06-05

Jun 5, 2025

10 months ago

Qwen3-235B-A22B-Instruct-2507

Jul 22, 2025

9 months ago

1mo newer

Knowledge Cutoff

When training data ends

Gemini 2.5 Pro Preview 06-05 has a documented knowledge cutoff of 2025-01-31, while Qwen3-235B-A22B-Instruct-2507's cutoff date is not specified.

We can confirm Gemini 2.5 Pro Preview 06-05's training data extends to 2025-01-31, but cannot make a direct comparison without Qwen3-235B-A22B-Instruct-2507's cutoff date.

Gemini 2.5 Pro Preview 06-05

Jan 2025

Qwen3-235B-A22B-Instruct-2507

—

Provider Availability

Gemini 2.5 Pro Preview 06-05 is available from Google. Qwen3-235B-A22B-Instruct-2507 is available from Fireworks, Novita.

Gemini 2.5 Pro Preview 06-05

google logo
Google
Input Price:Input: $1.25/1MOutput Price:Output: $10.00/1M

Qwen3-235B-A22B-Instruct-2507

fireworks logo
Fireworks
Input Price:Input: $0.15/1MOutput Price:Output: $0.80/1M
novita logo
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

Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
Higher Aider-Polyglot score (82.2% vs 57.3%)
Higher AIME 2025 score (88.0% vs 70.3%)
Higher GPQA score (86.4% vs 77.5%)
Alibaba Cloud / Qwen Team
Less expensive input tokens
Less expensive output tokens
Has open weights
Higher SimpleQA score (54.3% vs 54.0%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.5 Pro Preview 06-05
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507

FAQ

Common questions about Gemini 2.5 Pro Preview 06-05 vs Qwen3-235B-A22B-Instruct-2507

Gemini 2.5 Pro Preview 06-05 shows notably better performance in the majority of benchmarks. Gemini 2.5 Pro Preview 06-05 is made by Google 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.
Gemini 2.5 Pro Preview 06-05 scores Global-MMLU-Lite: 89.2%, AIME 2025: 88.0%, FACTS Grounding: 87.8%, GPQA: 86.4%, VideoMMMU: 83.6%. 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%.
Qwen3-235B-A22B-Instruct-2507 is 8.3x cheaper for input tokens. Gemini 2.5 Pro Preview 06-05 costs $1.25/M input and $10.00/M output via google. Qwen3-235B-A22B-Instruct-2507 costs $0.15/M input and $0.80/M output via fireworks.
Gemini 2.5 Pro Preview 06-05 supports 1.0M 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.
Key differences include context window (1.0M vs 262K), input pricing ($1.25 vs $0.15/M), multimodal support (yes vs no), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
Gemini 2.5 Pro Preview 06-05 is developed by Google and Qwen3-235B-A22B-Instruct-2507 is developed by Alibaba Cloud / Qwen Team.