Gemini 2.0 Flash vs GPT-4.1 vs Qwen3 32B
Too close to call on our weighted score (Gemini 2.0 Flash 65, GPT-4.1 63, Qwen3 32B 53). The right pick depends on what you value most.
GoogleGemini 2.0 Flash
65/100- ECI134.7
- Price—
- Context1.05M
OpenAIGPT-4.1
63/100- ECI136.8
- Price$2.00 / $8.00
- Context1.05M
Alibaba (Qwen)Qwen3 32B
53/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Gemini 2.0 Flash 65/100, GPT-4.1 63/100, Qwen3 32B 53/100), so choose by what matters most for your work: Qwen3 32B for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityQwen3 32BCapabilities Index (ECI): Qwen3 32B 138.5 · GPT-4.1 136.8 · Gemini 2.0 Flash 134.7
- Lowest priceQwen3 32BQwen3 32B $1.23 · GPT-4.1 $3.50 per 1M tokens (3:1 blend) · Gemini 2.0 Flash unpriced
- Longest contextGemini 2.0 Flash and GPT-4.1Gemini 2.0 Flash 1,048,576 · GPT-4.1 1,047,576 · Qwen3 32B 131,072 tokens
- Widest inputsGemini 2.0 FlashGemini 2.0 Flash: Text, Images, PDFs, Audio, Video · GPT-4.1: Text, Images, PDFs · Qwen3 32B: Text
- Self-hostingQwen3 32BPublishes downloadable weights
| Measure | Weight | Gemini 2.0 Flash | GPT-4.1 | Qwen3 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 59 | 61 | 64 |
| Inputs & features | 20% | 90 | 70 | 35 |
| Context window | 13% | 61 | 61 | 24 |
| Overall | 100% | 65/100 | 63/100 | 53/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.0 FlashGoogle | GPT-4.1OpenAI | Qwen3 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 134.7 | 136.8 | 138.5 (best) |
| ECI rank | #116 of 148 | #111 of 148 | #106 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 66.9% (best) | 65.7% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 6.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 38.3% | 66.9% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 48.5% | — |
| SimpleQA VerifiedShort factual questions | — | 31.1% | — |
| Price per million tokens | |||
| Input | — | $2.00 | $0.70 (best) |
| Output | — | $8.00 | $2.80 (best) |
| Cached input | — | $0.50 | — |
| Blended (3:1) | — | $3.50 | $1.23 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official OpenAI API | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,047,576 tokens | 131,072 tokens |
| Max output | 8,192 tokens | 32,768 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | Yes | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | — | gpt-4.1 | qwen3-32b |
| API providers | — | 25 (best) | 14 |
| Released | Dec 11, 2024 | Apr 14, 2025 | Apr 29, 2025 |
| Knowledge cutoff | Jun 2024 | Apr 2024 | Apr 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 2.0 Flash—
- GPT-4.1$36.00
- Qwen3 32B$12.60
Which should you choose?
Which is better: Gemini 2.0 Flash, GPT-4.1 or Qwen3 32B?
It is close. Our weighted score puts them within 2 points (Gemini 2.0 Flash 65/100, GPT-4.1 63/100, Qwen3 32B 53/100), so choose by what matters most for your work: Qwen3 32B for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Gemini 2.0 Flash, GPT-4.1 or Qwen3 32B?
Qwen3 32B is cheaper at $0.70 input / $2.80 output per million tokens (official Alibaba API price). GPT-4.1 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 $1.23 per million tokens for Qwen3 32B versus $3.50 for GPT-4.1 (2.9× as much). Gemini 2.0 Flash has no published per-token price.
Which scores higher on benchmarks?
Qwen3 32B scores higher on the Capabilities Index (ECI): Qwen3 32B 138.5 (#106 of 148), GPT-4.1 136.8 (#111 of 148) and Gemini 2.0 Flash 134.7 (#116 of 148). The confidence ranges of the top two overlap (135.1–140.4 vs 133.6–138.4), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.0 Flash and Qwen3 32B yet, so there is no like-for-like coding score. On overall capability, Qwen3 32B 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 2.0 Flash and GPT-4.1 have the largest context windows (1,048,576 and 1,047,576 tokens), against 131,072 for Qwen3 32B. Maximum output per response: Gemini 2.0 Flash up to 8,192, GPT-4.1 up to 32,768, Qwen3 32B up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.0 Flash accepts text, images, PDFs, audio and video; GPT-4.1 accepts text, images and PDFs; Qwen3 32B accepts text. Gemini 2.0 Flash handles the widest range of inputs.
Are any of these open source?
Qwen3 32B publishes its weights and can be self-hosted; Gemini 2.0 Flash and GPT-4.1 is proprietary.
Which is newer?
Qwen3 32B is the newest, released Apr 29, 2025. GPT-4.1 came out Apr 14, 2025; Gemini 2.0 Flash came out Dec 11, 2024. Knowledge cutoff: Gemini 2.0 Flash Jun 2024, GPT-4.1 Apr 2024, Qwen3 32B Apr 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.
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