Gemini 2.5 Pro Preview 06-05 vs Qwen3 235B-A22B Thinking 2507
Gemini 2.5 Pro Preview 06-05 comes out ahead, 48 to 42 on our weighted score, though Qwen3 235B-A22B Thinking 2507 is 3.5× cheaper per token.
- Our pick
GoogleGemini 2.5 Pro Preview 06-05
48/100- ECI—
- Price$1.22 / $9.75
- Context1.05M
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
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Make it a three-way comparison.
Gemini 2.5 Pro Preview 06-05 is our pick
Gemini 2.5 Pro Preview 06-05 is the better all-round choice, scoring 48/100 against Qwen3 235B-A22B Thinking 2507 (42). It leads on inputs & features and context window. Qwen3 235B-A22B Thinking 2507 wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · Gemini 2.5 Pro Preview 06-05 $3.35 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Pro Preview 06-05Gemini 2.5 Pro Preview 06-05 1,048,576 · Qwen3 235B-A22B Thinking 2507 262,144 tokens
- Widest inputsGemini 2.5 Pro Preview 06-05Gemini 2.5 Pro Preview 06-05: Text, Images, PDFs, Audio · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingQwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Gemini 2.5 Pro Preview 06-05 | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|
| Price | 50% | 24 | 49 |
| Inputs & features | 30% | 80 | 35 |
| Context window | 20% | 61 | 37 |
| Overall | 100% | 48/100 | 42/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.5 Pro Preview 06-05Google | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 143.8 |
| ECI rank | — | #93 of 165 |
| GPQA DiamondGraduate-level science questions | — | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 86.7% |
| SimpleQA VerifiedShort factual questions | — | 40.4% |
| Price per million tokens | ||
| Input | $1.22 | $0.30 (best) |
| Output | $9.75 | $2.90 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.35 | $0.95 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 11 providers |
| Limits | ||
| Context window | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 65,536 tokens | 81,920 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | Yes | No |
| Audio | Yes | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | OpenApache 2.0 |
| API model ID | — | — |
| API providers | 4 | 12 (best) |
| Released | Jun 5, 2025 | Jul 25, 2025 |
| Knowledge cutoff | Jan 31, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.5 Pro Preview 06-05$31.69
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: Gemini 2.5 Pro Preview 06-05 or Qwen3 235B-A22B Thinking 2507?
Gemini 2.5 Pro Preview 06-05 is the better all-round choice, scoring 48/100 against Qwen3 235B-A22B Thinking 2507 (42). It leads on inputs & features and context window. Qwen3 235B-A22B Thinking 2507 wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Gemini 2.5 Pro Preview 06-05 or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). Gemini 2.5 Pro Preview 06-05 costs $1.22 input / $9.75 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $3.35 for Gemini 2.5 Pro Preview 06-05 (3.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Gemini 2.5 Pro Preview 06-05 has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.5 Pro Preview 06-05 and Qwen3 235B-A22B Thinking 2507 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Pro Preview 06-05 has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507. Maximum output per response: Gemini 2.5 Pro Preview 06-05 up to 65,536, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Pro Preview 06-05 accepts text, images, PDFs and audio; Qwen3 235B-A22B Thinking 2507 accepts text. Gemini 2.5 Pro Preview 06-05 handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Gemini 2.5 Pro Preview 06-05 is proprietary.
Which is newer?
Qwen3 235B-A22B Thinking 2507 is the newest, released Jul 25, 2025. Gemini 2.5 Pro Preview 06-05 came out Jun 5, 2025. Knowledge cutoff: Gemini 2.5 Pro Preview 06-05 Jan 31, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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