DeepSeek R1 0528 vs Kimi K2.5 vs Gemini 2.5 Flash
Gemini 2.5 Flash comes out ahead, 66 to 62 and 53 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
Moonshot AIKimi K2.5
62/100- ECI148.1
- Price$0.60 / $3.00
- Context262K
- 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 Kimi K2.5 (62) and DeepSeek R1 0528 (53). It leads on price, inputs & features and context window. Kimi K2.5 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.5Capabilities Index (ECI): Kimi K2.5 148.1 · DeepSeek R1 0528 141.2 · Gemini 2.5 Flash 140.6
- Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · DeepSeek R1 0528 $0.932 · Kimi K2.5 $1.20 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · Kimi K2.5 262,144 · DeepSeek R1 0528 163,840 tokens
- Widest inputsGemini 2.5 FlashDeepSeek R1 0528: Text · Kimi K2.5: Text, Images, Video · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingDeepSeek R1 0528 and Kimi K2.5Publishes downloadable weights (MIT)
| Measure | Weight | DeepSeek R1 0528 | Kimi K2.5 | Gemini 2.5 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 66 | 74 | 65 |
| Price | 25% | 49 | 44 | 51 |
| Inputs & features | 15% | 35 | 70 | 100 |
| Context window | 10% | 28 | 37 | 61 |
| Overall | 100% | 53/100 | 62/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek R1 0528DeepSeek | Kimi K2.5Moonshot AI | Gemini 2.5 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 141.2 | 148.1 (best) | 140.6 |
| ECI rank | #108 of 165 | #68 of 165 (best) | #110 of 165 |
| GPQA DiamondGraduate-level science questions | 76.3% | 87.6% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% | 92.2% (best) | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 73.8% | — |
| SimpleQA VerifiedShort factual questions | — | 34.3% | — |
| Price per million tokens | |||
| Input | $0.512 | $0.60 | $0.30 (best) |
| Output | $2.19 (best) | $3.00 | $2.50 |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $0.932 | $1.20 | $0.85 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 16 providers | Median of 33 providers | Official Google API |
| Limits | |||
| Context window | 163,840 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenMIT | Open | Proprietary |
| API model ID | — | — | gemini-2.5-flash |
| API providers | 16 | 33 (best) | 32 |
| Released | May 28, 2025 | Jan 27, 2026 | Jun 17, 2025 |
| Knowledge cutoff | — | Jan 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.
- DeepSeek R1 0528$9.50
- Kimi K2.5$12.00
- Gemini 2.5 Flash$8.00
Which should you choose?
Which is better: DeepSeek R1 0528, Kimi K2.5 or Gemini 2.5 Flash?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and DeepSeek R1 0528 (53). It leads on price, inputs & features and context window. Kimi K2.5 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek R1 0528, Kimi K2.5 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); Kimi K2.5 costs $0.60 input / $3.00 output per million tokens (median across 33 API providers). 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 $1.20 for Kimi K2.5 (1.4× as much).
Which scores higher on benchmarks?
Kimi K2.5 scores higher on the Capabilities Index (ECI): Kimi K2.5 148.1 (#68 of 165), DeepSeek R1 0528 141.2 (#108 of 165) and Gemini 2.5 Flash 140.6 (#110 of 165). Their confidence ranges do not overlap (146.3–149.5 vs 139.0–142.9), so the gap is a real one.
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, Kimi K2.5 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 has the largest context window at 1,048,576 tokens, against 262,144 for Kimi K2.5 and 163,840 for DeepSeek R1 0528. Maximum output per response: DeepSeek R1 0528 up to 32,768, Kimi K2.5 up to 262,144, Gemini 2.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
DeepSeek R1 0528 accepts text; Kimi K2.5 accepts text, images and video; 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 and Kimi K2.5 publishes its weights (MIT) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
Kimi K2.5 is the newest, released Jan 27, 2026. Gemini 2.5 Flash came out Jun 17, 2025; DeepSeek R1 0528 came out May 28, 2025. Knowledge cutoff: Kimi K2.5 Jan 2025, 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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