o3 vs Qwen3 Max vs Gemini 2.5 Pro
Gemini 2.5 Pro comes out ahead, 63 to 58 and 50 on our weighted score, though Qwen3 Max is 30% cheaper per token.
OpenAIo3
58/100- ECI146.9
- Price$2.00 / $8.00
- Context200K
Alibaba (Qwen)Qwen3 Max
50/100- ECI142.4
- Price$1.20 / $6.00
- Context262K
- Our pick
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
Gemini 2.5 Pro is our pick
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against o3 (58) and Qwen3 Max (50). It leads on inputs & features and context window. o3 wins on capability. Qwen3 Max wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- Capabilityo3Capabilities Index (ECI): o3 146.9 · Gemini 2.5 Pro 145.3 · Qwen3 Max 142.4
- Lowest priceQwen3 MaxQwen3 Max $2.40 · Gemini 2.5 Pro $3.44 · o3 $3.50 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · Qwen3 Max 262,144 · o3 200,000 tokens
- Widest inputsGemini 2.5 Proo3: Text, Images, PDFs · Qwen3 Max: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | o3 | Qwen3 Max | Gemini 2.5 Pro |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 68 | 72 |
| Price | 25% | 24 | 32 | 24 |
| Inputs & features | 15% | 80 | 25 | 100 |
| Context window | 10% | 32 | 37 | 61 |
| Overall | 100% | 58/100 | 50/100 | 63/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o3OpenAI | Qwen3 MaxAlibaba (Qwen) | Gemini 2.5 ProGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.9 (best) | 142.4 | 145.3 |
| ECI rank | #63 of 148 (best) | #91 of 148 | #78 of 148 |
| GPQA DiamondGraduate-level science questions | 81.8% | 72.6% | 85.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 33.3% (best) | 19.0% | 24.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.4% | 73.3% | 84.7% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 62.3% (best) | — | 57.6% |
| SimpleQA VerifiedShort factual questions | 49.4% (best) | 48.8% | — |
| Price per million tokens | |||
| Input | $2.00 | $1.20 (best) | $1.25 |
| Output | $8.00 | $6.00 (best) | $10.00 |
| Cached input | $0.50 | — | $0.125 (best) |
| Blended (3:1) | $3.50 | $2.40 (best) | $3.44 |
| Long-context rate | Same rate | Same rate | Over 200K: $2.50 / $15.00 |
| Price source | Official OpenAI API | Official Alibaba API | Official Google API |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 100,000 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | o3 | qwen3-max | gemini-2.5-pro |
| API providers | 18 | 16 | 22 (best) |
| Released | Apr 16, 2025 | Sep 23, 2025 | Jun 17, 2025 |
| Knowledge cutoff | May 2024 | Apr 2025 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3$36.00
- Qwen3 Max$24.00
- Gemini 2.5 Pro$32.50
Which should you choose?
Which is better: o3, Qwen3 Max or Gemini 2.5 Pro?
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against o3 (58) and Qwen3 Max (50). It leads on inputs & features and context window. o3 wins on capability. Qwen3 Max wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, o3, Qwen3 Max or Gemini 2.5 Pro?
Qwen3 Max is cheaper at $1.20 input / $6.00 output per million tokens (official Alibaba API price). Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price); o3 costs $2.00 input / $8.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $2.40 per million tokens for Qwen3 Max versus $3.44 for Gemini 2.5 Pro (1.4× as much) and $3.50 for o3 (1.5× as much).
Which scores higher on benchmarks?
o3 scores higher on the Capabilities Index (ECI): o3 146.9 (#63 of 148), Gemini 2.5 Pro 145.3 (#78 of 148) and Qwen3 Max 142.4 (#91 of 148). The confidence ranges of the top two overlap (144.9–148.6 vs 143.6–146.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemini 2.5 Pro 85.3%, o3 81.8%, Qwen3 Max 72.6%; FrontierMath Tiers 1–3 — o3 33.3%, Gemini 2.5 Pro 24.6%, Qwen3 Max 19.0%; OTIS Mock AIME 2024–2025 — Gemini 2.5 Pro 84.7%, o3 84.4%, Qwen3 Max 73.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 Max yet, so there is no like-for-like coding score. On overall capability, o3 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3 Max and 200,000 for o3. Maximum output per response: o3 up to 100,000, Qwen3 Max up to 65,536, Gemini 2.5 Pro up to 65,536 tokens.
Which can read images, PDFs, audio or video?
o3 accepts text, images and PDFs; Qwen3 Max accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
No. o3, Qwen3 Max and Gemini 2.5 Pro are proprietary and only available through APIs and apps.
Which is newer?
Qwen3 Max is the newest, released Sep 23, 2025. Gemini 2.5 Pro came out Jun 17, 2025; o3 came out Apr 16, 2025. Knowledge cutoff: o3 May 2024, Qwen3 Max Apr 2025, Gemini 2.5 Pro Jan 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 Oct 4, 2026.
