GPT-4.1 vs Gemini 2.5 Flash vs DeepSeek-R1
Gemini 2.5 Flash comes out ahead, 66 to 52 and 48 on our weighted score, and it is the cheaper option too.
OpenAIGPT-4.1
52/100- ECI136.7
- Price$2.00 / $8.00
- Context1.05M
- Our pick
GoogleGemini 2.5 Flash
66/100- ECI140.6
- Price$0.30 / $2.50
- Context1.05M
DeepSeekDeepSeek-R1
48/100- ECI138.8
- Price$0.70 / $4.88
- Context128K
Gemini 2.5 Flash is our pick
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against GPT-4.1 (52) and DeepSeek-R1 (48). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 2.5 FlashCapabilities Index (ECI): Gemini 2.5 Flash 140.6 · DeepSeek-R1 138.8 · GPT-4.1 136.7
- Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · DeepSeek-R1 $1.75 · GPT-4.1 $3.50 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash and GPT-4.1Gemini 2.5 Flash 1,048,576 · GPT-4.1 1,047,576 · DeepSeek-R1 128,000 tokens
- Widest inputsGemini 2.5 FlashGPT-4.1: Text, Images, PDFs · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video · DeepSeek-R1: Text
- Self-hostingDeepSeek-R1Publishes downloadable weights
| Measure | Weight | GPT-4.1 | Gemini 2.5 Flash | DeepSeek-R1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 60 | 65 | 63 |
| Price | 25% | 23 | 51 | 37 |
| Inputs & features | 15% | 70 | 100 | 35 |
| Context window | 10% | 61 | 61 | 24 |
| Overall | 100% | 52/100 | 66/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-4.1OpenAI | Gemini 2.5 FlashGoogle | DeepSeek-R1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 136.7 | 140.6 (best) | 138.8 |
| ECI rank | #125 of 165 | #110 of 165 (best) | #119 of 165 |
| GPQA DiamondGraduate-level science questions | 66.9% | — | 71.7% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 6.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 38.3% | — | 53.3% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 48.5% | — | — |
| SimpleQA VerifiedShort factual questions | 31.1% | — | — |
| Price per million tokens | |||
| Input | $2.00 | $0.30 (best) | $0.70 |
| Output | $8.00 | $2.50 (best) | $4.88 |
| Cached input | $0.50 | $0.03 (best) | — |
| Blended (3:1) | $3.50 | $0.85 (best) | $1.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Google API | Median of 11 providers |
| Limits | |||
| Context window | 1,047,576 tokens | 1,048,576 tokens (best) | 128,000 tokens |
| Max output | 32,768 tokens | 65,536 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-4.1 | gemini-2.5-flash | — |
| API providers | 31 | 32 (best) | 12 |
| Released | Apr 14, 2025 | Jun 17, 2025 | Jan 20, 2025 |
| Knowledge cutoff | Apr 2024 | Jan 2025 | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-4.1$36.00
- Gemini 2.5 Flash$8.00
- DeepSeek-R1$16.76
Which should you choose?
Which is better: GPT-4.1, Gemini 2.5 Flash or DeepSeek-R1?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against GPT-4.1 (52) and DeepSeek-R1 (48). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-4.1, Gemini 2.5 Flash or DeepSeek-R1?
Gemini 2.5 Flash is cheaper at $0.30 input / $2.50 output per million tokens (official Google API price). DeepSeek-R1 costs $0.70 input / $4.88 output per million tokens (median across 11 API providers); 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 $0.85 per million tokens for Gemini 2.5 Flash versus $1.75 for DeepSeek-R1 (2.1× as much) and $3.50 for GPT-4.1 (4.1× as much).
Which scores higher on benchmarks?
Gemini 2.5 Flash scores higher on the Capabilities Index (ECI): Gemini 2.5 Flash 140.6 (#110 of 165), DeepSeek-R1 138.8 (#119 of 165) and GPT-4.1 136.7 (#125 of 165). The confidence ranges of the top two overlap (138.4–142.1 vs 136.5–140.2), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.5 Flash and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Gemini 2.5 Flash 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 Flash and GPT-4.1 have the largest context windows (1,048,576 and 1,047,576 tokens), against 128,000 for DeepSeek-R1. Maximum output per response: GPT-4.1 up to 32,768, Gemini 2.5 Flash up to 65,536, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GPT-4.1 accepts text, images and PDFs; Gemini 2.5 Flash accepts text, images, PDFs, audio and video; DeepSeek-R1 accepts text. Gemini 2.5 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 publishes its weights and can be self-hosted; GPT-4.1 and Gemini 2.5 Flash is proprietary.
Which is newer?
Gemini 2.5 Flash is the newest, released Jun 17, 2025. GPT-4.1 came out Apr 14, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: GPT-4.1 Apr 2024, Gemini 2.5 Flash Jan 2025, DeepSeek-R1 Jul 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 Nov 2, 2026.
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