DeepSeek R1 vs Gemini 2.5 Pro
DeepSeek R1 (2025) and Gemini 2.5 Pro (2025) are frontier-tier reasoning models from DeepSeek and Google DeepMind. DeepSeek R1 ships a 128k-token context window, while Gemini 2.5 Pro ships a 1m-token context window. On SWE-bench Verified, Gemini 2.5 Pro leads by 14.6 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Gemini 2.5 Pro fits 8x more tokens; pick it for long-context work and DeepSeek R1 for tighter calls.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 | Gemini 2.5 Pro |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 128k | 1m |
| Cheapest output | $0.30/1M tokens | $10/1M tokens |
| Provider routes | 14 tracked | 4 tracked |
| Shared benchmarks | 5 rows | SWE-bench Verified leader |
Decision tradeoffs
- DeepSeek R1 has the lower cheapest tracked output price at $0.30/1M tokens.
- DeepSeek R1 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags DeepSeek R1 for Coding, RAG, and Agents.
- Gemini 2.5 Pro holds a shared-benchmark lead on SWE-bench Verified, ahead by 14.6 points.
- Gemini 2.5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemini 2.5 Pro uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Gemini 2.5 Pro for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek R1
$155
Cheapest tracked route/tier: Bitdeer AI
Gemini 2.5 Pro
$3,500
Cheapest tracked route/tier: Google AI Studio <=200K tokens
Estimated monthly gap: $3,345. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on GCP Vertex AI, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- Gemini 2.5 Pro is $9.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Gemini 2.5 Pro adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on OpenRouter, GCP Vertex AI, and Vercel AI Gateway; start route-level A/B tests there.
- DeepSeek R1 is $9.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-20 | 2025-06-17 |
| Context window | 128k | 1m |
| Parameters | 671B, 37B Active | — |
| Architecture | decoder only | decoder only |
| License | MIT(OSI) | Proprietary |
| Openness | Open source | Proprietary |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | 2023-12 | 2025-01 |
Pricing and availability
| Pricing attribute | DeepSeek R1 | Gemini 2.5 Pro |
|---|---|---|
| Input price | $0.10/1M tokens |
|
| Output price | $0.30/1M tokens |
|
| Providers |
Capabilities
| Capability | DeepSeek R1 | Gemini 2.5 Pro |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek R1 | Gemini 2.5 Pro |
|---|---|---|
| SWE-bench Verified | 49.2 | 63.8 |
| Google-Proof Q&A | 71.5 | 86.4 |
| HumanEval | 89.9 | 93.1 |
| Aider Polyglot | 56.9 | 83.1 |
| Chatbot Arena | 1372.0 | 1398.0 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek R1 at 49.2 and Gemini 2.5 Pro at 63.8, with Gemini 2.5 Pro ahead by 14.6 points; Google-Proof Q&A has DeepSeek R1 at 71.5 and Gemini 2.5 Pro at 86.4, with Gemini 2.5 Pro ahead by 14.9 points; HumanEval has DeepSeek R1 at 89.9 and Gemini 2.5 Pro at 93.1, with Gemini 2.5 Pro ahead by 3.2 points. The largest visible gap is 14.9 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Gemini 2.5 Pro, multimodal input: Gemini 2.5 Pro, function calling: Gemini 2.5 Pro, and tool use: Gemini 2.5 Pro. Both models share reasoning mode, structured outputs, and code execution, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek R1 lists $0.10/1M input and $0.30/1M output tokens on the cheapest tracked provider, while Gemini 2.5 Pro lists tiered pricing: <=200K tokens is $1.25/1M input and $10/1M output; <=200K tokens is $1.25/1M input and $10/1M output; 0-200,001t is $1.25/1M input and $10/1M output; >200K tokens is $2.50/1M input and $15/1M output; >200K tokens is $2.50/1M input and $15/1M output; 200,001t+ is $2.50/1M input and $15/1M output. A 70/30 input-output blend puts DeepSeek R1 lower by about $3.71 per million blended tokens. For tiered rows, this cheapest-track view can understate interactive or fast-lane spend, so compare the tier you will actually use. Availability is 14 providers versus 4, so concentration risk also matters.
Choose DeepSeek R1 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Gemini 2.5 Pro when coding workflow support and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek R1 or Gemini 2.5 Pro?
Gemini 2.5 Pro supports 1m tokens, while DeepSeek R1 supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek R1 or Gemini 2.5 Pro?
DeepSeek R1 lists $0.10/1M input and $0.30/1M output tokens on the cheapest tracked provider. Gemini 2.5 Pro lists tiered pricing: <=200K tokens is $1.25/1M input and $10/1M output; <=200K tokens is $1.25/1M input and $10/1M output; 0-200,001t is $1.25/1M input and $10/1M output; >200K tokens is $2.50/1M input and $15/1M output; >200K tokens is $2.50/1M input and $15/1M output; 200,001t+ is $2.50/1M input and $15/1M output. Compare the tier you will actually use; cheap async pricing can overstate savings for interactive workflows. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 or Gemini 2.5 Pro open source?
DeepSeek R1 is listed under MIT. Gemini 2.5 Pro is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, DeepSeek R1 or Gemini 2.5 Pro?
Gemini 2.5 Pro has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, DeepSeek R1 or Gemini 2.5 Pro?
Gemini 2.5 Pro has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek R1 and Gemini 2.5 Pro?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-05. Data sourced from public model cards and provider documentation.