Apertus 70B vs DeepSeek R1 0528 vs Gemini 2.5 Flash
Gemini 2.5 Flash comes out ahead, 68 to 41 and 32 on our weighted score, and it is the cheaper option too.
Swiss AI
Apertus 70B
32/100- ECI—
- Price$0.82 / $2.42
- Context66K
DeepSeekDeepSeek R1 0528
41/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
- Our pick
GoogleGemini 2.5 Flash
68/100- 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 68/100 against DeepSeek R1 0528 (41) and Apertus 70B (32). It leads on price, inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · DeepSeek R1 0528 $0.932 · Apertus 70B $1.22 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · DeepSeek R1 0528 163,840 · Apertus 70B 65,536 tokens
- Widest inputsGemini 2.5 FlashApertus 70B: Text · DeepSeek R1 0528: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingApertus 70B and DeepSeek R1 0528Publishes downloadable weights (Apache-2.0 and MIT)
| Measure | Weight | Apertus 70B | DeepSeek R1 0528 | Gemini 2.5 Flash |
|---|---|---|---|---|
| Price | 50% | 44 | 49 | 51 |
| Inputs & features | 30% | 25 | 35 | 100 |
| Context window | 20% | 12 | 28 | 61 |
| Overall | 100% | 32/100 | 41/100 | 68/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Apertus 70BSwiss AI | DeepSeek R1 0528DeepSeek | Gemini 2.5 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 141.2 (best) | 140.6 |
| ECI rank | — | #108 of 165 (best) | #110 of 165 |
| GPQA DiamondGraduate-level science questions | — | 76.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 66.4% | — |
| Price per million tokens | |||
| Input | $0.82 | $0.512 | $0.30 (best) |
| Output | $2.42 | $2.19 (best) | $2.50 |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $1.22 | $0.932 | $0.85 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 16 providers | Official Google API |
| Limits | |||
| Context window | 65,536 tokens | 163,840 tokens | 1,048,576 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | OpenMIT | Proprietary |
| API model ID | — | — | gemini-2.5-flash |
| API providers | 3 | 16 | 32 (best) |
| Released | Sep 2, 2025 | May 28, 2025 | Jun 17, 2025 |
| Knowledge cutoff | Sep 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.
- Apertus 70B$13.04
- DeepSeek R1 0528$9.50
- Gemini 2.5 Flash$8.00
Which should you choose?
Which is better: Apertus 70B, DeepSeek R1 0528 or Gemini 2.5 Flash?
Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek R1 0528 (41) and Apertus 70B (32). It leads on price, inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Apertus 70B, DeepSeek R1 0528 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); Apertus 70B costs $0.82 input / $2.42 output per million tokens (median across 3 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 $1.22 for Apertus 70B (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 70B has not been scored yet, DeepSeek R1 0528 has an ECI of 141.2 and Gemini 2.5 Flash has an ECI of 140.6.
Which is better for coding?
There are no published SWE-bench Verified results for Apertus 70B, DeepSeek R1 0528 and Gemini 2.5 Flash yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 163,840 for DeepSeek R1 0528 and 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, DeepSeek R1 0528 up to 32,768, Gemini 2.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; DeepSeek R1 0528 accepts text; 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?
Apertus 70B and DeepSeek R1 0528 publishes its weights (Apache-2.0 and MIT) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
Apertus 70B is the newest, released Sep 2, 2025. Gemini 2.5 Flash came out Jun 17, 2025; DeepSeek R1 0528 came out May 28, 2025. Knowledge cutoff: Apertus 70B Sep 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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