Gemini 2.0 Flash vs GPT-4.1 vs Claude Sonnet 3.7
Too close to call on our weighted score (Gemini 2.0 Flash 65, GPT-4.1 63, Claude Sonnet 3.7 63). 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
AnthropicClaude Sonnet 3.7
63/100- ECI141.2
- Price$3.00 / $15.00
- Context200K
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, Claude Sonnet 3.7 63/100), so choose by what matters most for your work: Claude Sonnet 3.7 for raw capability and GPT-4.1 on price. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityClaude Sonnet 3.7Capabilities Index (ECI): Claude Sonnet 3.7 141.2 · GPT-4.1 136.8 · Gemini 2.0 Flash 134.7
- Lowest priceGPT-4.1GPT-4.1 $3.50 · Claude Sonnet 3.7 $6.00 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 · Claude Sonnet 3.7 200,000 tokens
- Widest inputsGemini 2.0 FlashGemini 2.0 Flash: Text, Images, PDFs, Audio, Video · GPT-4.1: Text, Images, PDFs · Claude Sonnet 3.7: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Gemini 2.0 Flash | GPT-4.1 | Claude Sonnet 3.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 59 | 61 | 67 |
| Inputs & features | 20% | 90 | 70 | 70 |
| Context window | 13% | 61 | 61 | 32 |
| Overall | 100% | 65/100 | 63/100 | 63/100 |
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 | Claude Sonnet 3.7Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 134.7 | 136.8 | 141.2 (best) |
| ECI rank | #116 of 148 | #111 of 148 | #96 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 66.9% | 79.7% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 6.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 38.3% | 57.8% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 48.5% | 61.0% (best) |
| SimpleQA VerifiedShort factual questions | — | 31.1% | — |
| Price per million tokens | |||
| Input | — | $2.00 (best) | $3.00 |
| Output | — | $8.00 (best) | $15.00 |
| Cached input | — | $0.50 | — |
| Blended (3:1) | — | $3.50 (best) | $6.00 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official OpenAI API | Median of 3 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,047,576 tokens | 200,000 tokens |
| Max output | 8,192 tokens | 32,768 tokens | 64,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | Yes |
| 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 | Proprietary |
| API model ID | — | gpt-4.1 | — |
| API providers | — | 25 (best) | 3 |
| Released | Dec 11, 2024 | Apr 14, 2025 | Feb 19, 2025 |
| Knowledge cutoff | Jun 2024 | Apr 2024 | Oct 31, 2024 |
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
- Claude Sonnet 3.7$60.00
Which should you choose?
Which is better: Gemini 2.0 Flash, GPT-4.1 or Claude Sonnet 3.7?
It is close. Our weighted score puts them within 2 points (Gemini 2.0 Flash 65/100, GPT-4.1 63/100, Claude Sonnet 3.7 63/100), so choose by what matters most for your work: Claude Sonnet 3.7 for raw capability and GPT-4.1 on price. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Gemini 2.0 Flash, GPT-4.1 or Claude Sonnet 3.7?
GPT-4.1 is cheaper at $2.00 input / $8.00 output per million tokens (official OpenAI API price). Claude Sonnet 3.7 costs $3.00 input / $15.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $3.50 per million tokens for GPT-4.1 versus $6.00 for Claude Sonnet 3.7 (1.7× as much). Gemini 2.0 Flash has no published per-token price.
Which scores higher on benchmarks?
Claude Sonnet 3.7 scores higher on the Capabilities Index (ECI): Claude Sonnet 3.7 141.2 (#96 of 148), GPT-4.1 136.8 (#111 of 148) and Gemini 2.0 Flash 134.7 (#116 of 148). Their confidence ranges do not overlap (138.7–142.9 vs 133.6–138.4), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.0 Flash yet, so there is no like-for-like coding score. On overall capability, Claude Sonnet 3.7 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 200,000 for Claude Sonnet 3.7. Maximum output per response: Gemini 2.0 Flash up to 8,192, GPT-4.1 up to 32,768, Claude Sonnet 3.7 up to 64,000 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; Claude Sonnet 3.7 accepts text, images and PDFs. Gemini 2.0 Flash handles the widest range of inputs.
Are any of these open source?
No. Gemini 2.0 Flash, GPT-4.1 and Claude Sonnet 3.7 are proprietary and only available through APIs and apps.
Which is newer?
GPT-4.1 is the newest, released Apr 14, 2025. Claude Sonnet 3.7 came out Feb 19, 2025; Gemini 2.0 Flash came out Dec 11, 2024. Knowledge cutoff: Gemini 2.0 Flash Jun 2024, GPT-4.1 Apr 2024, Claude Sonnet 3.7 Oct 31, 2024.
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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