Model comparison · Updated May 2026
DeepSeek R1 vs Gemini 2.5 Pro: Price, Context, Benchmarks (2026)
A direct, dated comparison of DeepSeek R1 (DeepSeek) and Gemini 2.5 Pro (Google). Every number below is sourced from official provider docs and public benchmarks. If you need to make this decision today, the verdict is at the top.
30-second verdict
- Cheaper: DeepSeek R1 (input $0.55 vs $1.25 per 1M tokens).
- Longer context: Gemini 2.5 Pro at 2M vs 128K.
- Stronger on SWE-bench Verified: Gemini 2.5 Pro (~60% vs ~52%).
- Higher LMArena: Gemini 2.5 Pro (1420 vs 1418).
- Open weights: DeepSeek R1 can be self-hosted.
Specs side-by-side
| Spec | DeepSeek R1 | Gemini 2.5 Pro |
|---|---|---|
| Vendor | DeepSeek | |
| Input price (per 1M tokens) | $0.55 | $1.25 |
| Output price | $2.19 | $10.00 |
| Context window | 128K | 2M |
| Release date | 2025-01-20 | 2025-06-17 |
| SWE-bench Verified | ~52% | ~60% |
| HumanEval | ~93% | ~92% |
| LMArena (approx) | 1418 | 1420 |
| Open weights | Yes | No |
| Capabilities | reasoning, code, cheap | reasoning, code, vision |
Pricing from official DeepSeek and Google docs. Benchmark numbers from SWE-bench Verified, HumanEval, and LMArena public leaderboards as of May 2026.
DeepSeek R1 — strengths and weaknesses
Strengths. Best price-to-quality, open weights, strong math + code, self-hostable.
Weaknesses. Weaker tool calling, smaller context, China-hosted official API.
Best for. Cost-sensitive production, batch jobs, self-hosted privacy use.
Gemini 2.5 Pro — strengths and weaknesses
Strengths. Largest context window (2M), strong multimodal, generous AI Studio free tier.
Weaknesses. Recall drops past 500K, weaker on agentic edits than Claude / GPT.
Best for. Whole-repo Q&A, long PDFs, multimodal, free prototyping.
Which one should you pick?
Pick DeepSeek R1 if: cost-sensitive production, batch jobs, self-hosted privacy use.
Pick Gemini 2.5 Pro if: whole-repo q&a, long pdfs, multimodal, free prototyping.
Use both if: you're building an agent or content pipeline. Route the high-stakes / hard-reasoning calls to whichever scores higher on the axis you care about, and the bulk / cheap calls to the other. Most production AI products run a 2-3 model router rather than betting on one.
Try them side-by-side
The Check.AI comparison tool lets you put both models in one table with all the numbers, switch capability filters, and share the resulting URL with your team.