DeepSeek R1 0528 vs GPT-4.1 vs Gemini 2.5 Flash
Gemini 2.5 Flash comes out ahead, 66 to 53 and 52 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
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
Gemini 2.5 Flash is our pick
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against DeepSeek R1 0528 (53) and GPT-4.1 (52). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek R1 0528Capabilities Index (ECI): DeepSeek R1 0528 141.2 · Gemini 2.5 Flash 140.6 · GPT-4.1 136.7
- Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · DeepSeek R1 0528 $0.932 · 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 0528 163,840 tokens
- Widest inputsGemini 2.5 FlashDeepSeek R1 0528: Text · GPT-4.1: Text, Images, PDFs · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingDeepSeek R1 0528Publishes downloadable weights (MIT)
| Measure | Weight | DeepSeek R1 0528 | GPT-4.1 | Gemini 2.5 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 66 | 60 | 65 |
| Price | 25% | 49 | 23 | 51 |
| Inputs & features | 15% | 35 | 70 | 100 |
| Context window | 10% | 28 | 61 | 61 |
| Overall | 100% | 53/100 | 52/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek R1 0528DeepSeek | GPT-4.1OpenAI | Gemini 2.5 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 141.2 (best) | 136.7 | 140.6 |
| ECI rank | #108 of 165 (best) | #125 of 165 | #110 of 165 |
| GPQA DiamondGraduate-level science questions | 76.3% (best) | 66.9% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 6.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% (best) | 38.3% | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 48.5% | — |
| SimpleQA VerifiedShort factual questions | — | 31.1% | — |
| Price per million tokens | |||
| Input | $0.512 | $2.00 | $0.30 (best) |
| Output | $2.19 (best) | $8.00 | $2.50 |
| Cached input | — | $0.50 | $0.03 (best) |
| Blended (3:1) | $0.932 | $3.50 | $0.85 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 16 providers | Official OpenAI API | Official Google API |
| Limits | |||
| Context window | 163,840 tokens | 1,047,576 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 32,768 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenMIT | Proprietary | Proprietary |
| API model ID | — | gpt-4.1 | gemini-2.5-flash |
| API providers | 16 | 31 | 32 (best) |
| Released | May 28, 2025 | Apr 14, 2025 | Jun 17, 2025 |
| Knowledge cutoff | — | Apr 2024 | 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.
- DeepSeek R1 0528$9.50
- GPT-4.1$36.00
- Gemini 2.5 Flash$8.00
Which should you choose?
Which is better: DeepSeek R1 0528, GPT-4.1 or Gemini 2.5 Flash?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against DeepSeek R1 0528 (53) and GPT-4.1 (52). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek R1 0528, GPT-4.1 or Gemini 2.5 Flash?
Gemini 2.5 Flash is cheaper at $0.30 input / $2.50 output per million tokens (official Google API price). DeepSeek R1 0528 costs $0.512 input / $2.19 output per million tokens (median across 16 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 $0.932 for DeepSeek R1 0528 (1.1× as much) and $3.50 for GPT-4.1 (4.1× as much).
Which scores higher on benchmarks?
DeepSeek R1 0528 scores higher on the Capabilities Index (ECI): DeepSeek R1 0528 141.2 (#108 of 165), Gemini 2.5 Flash 140.6 (#110 of 165) and GPT-4.1 136.7 (#125 of 165). The confidence ranges of the top two overlap (139.0–142.9 vs 138.4–142.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek R1 0528 and Gemini 2.5 Flash yet, so there is no like-for-like coding score. On overall capability, DeepSeek R1 0528 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 163,840 for DeepSeek R1 0528. Maximum output per response: DeepSeek R1 0528 up to 32,768, GPT-4.1 up to 32,768, Gemini 2.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
DeepSeek R1 0528 accepts text; GPT-4.1 accepts text, images and PDFs; Gemini 2.5 Flash accepts text, images, PDFs, audio and video. Gemini 2.5 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek R1 0528 publishes its weights (MIT) 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. DeepSeek R1 0528 came out May 28, 2025; GPT-4.1 came out Apr 14, 2025. Knowledge cutoff: GPT-4.1 Apr 2024, Gemini 2.5 Flash 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 Nov 2, 2026.
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