DeepSeek V3.1 vs Gemini 2.5 Pro
DeepSeek V3.1 (2025) and Gemini 2.5 Pro (2025) are frontier reasoning models from DeepSeek and Google DeepMind. DeepSeek V3.1 ships a 64k-token context window, while Gemini 2.5 Pro ships a 1m-token context window. On MMLU PRO, Gemini 2.5 Pro leads by 2.9 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 16x more tokens; pick it for long-context work and DeepSeek V3.1 for tighter calls.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.1 | Gemini 2.5 Pro |
|---|---|---|
| Best for | multimodal apps and provider-routed production | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, Agents, and Vision | Coding, RAG, and Agents |
| Context window | 64k | 1m |
| Cheapest output | $1/1M tokens | $10/1M tokens |
| Provider routes | 8 tracked | 4 tracked |
| Shared benchmarks | 2 shared | MMLU PRO leader |
Decision tradeoffs
- DeepSeek V3.1 holds a shared-benchmark lead on SWE-bench Verified, ahead by 2.2 points.
- DeepSeek V3.1 has the lower cheapest tracked output price at $1/1M tokens.
- DeepSeek V3.1 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags DeepSeek V3.1 for Coding, Agents, and Vision.
- Gemini 2.5 Pro holds a shared-benchmark lead on MMLU PRO, ahead by 2.9 points.
- Gemini 2.5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemini 2.5 Pro uniquely exposes Reasoning, Function calling, and Tool use 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 V3.1
$466
Cheapest tracked route/tier: Novita AI
Gemini 2.5 Pro
$3,500
Cheapest tracked route/tier: Google AI Studio <=200K tokens
Estimated monthly gap: $3,034. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- Gemini 2.5 Pro is $9/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Gemini 2.5 Pro adds Reasoning, Function calling, and Tool use in local capability data.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- DeepSeek V3.1 is $9/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-21 | 2025-06-17 |
| Context window | 64k | 1m |
| Parameters | 671B total, 37B active (MoE) | — |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Proprietary |
| Openness | Open source | Proprietary |
| Weights | Unknown | Not released |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | - | 2025-01 |
Pricing and availability
| Pricing attribute | DeepSeek V3.1 | Gemini 2.5 Pro |
|---|---|---|
| Input price | $0.27/1M tokens |
|
| Output price | $1/1M tokens |
|
| Providers |
Capabilities
| Capability | DeepSeek V3.1 | Gemini 2.5 Pro |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | 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 V3.1 | Gemini 2.5 Pro |
|---|---|---|
| MMLU PRO | 83.3 | 86.2 |
| SWE-bench Verified | 66.0 | 63.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Gemini 2.5 Pro at 86.2, with Gemini 2.5 Pro ahead by 2.9 points; SWE-bench Verified has DeepSeek V3.1 at 66 and Gemini 2.5 Pro at 63.8, with DeepSeek V3.1 ahead by 2.2 points. The largest visible gap is 2.9 points on MMLU PRO, 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 reasoning mode: Gemini 2.5 Pro, function calling: Gemini 2.5 Pro, and tool use: Gemini 2.5 Pro. Both models share vision, multimodal input, 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 V3.1 lists $0.27/1M input and $1/1M 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 $2.50/1M input and $15/1M output. A 70/30 input-output blend puts DeepSeek V3.1 lower by about $3.39 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 8 providers versus 4, so concentration risk also matters.
Choose DeepSeek V3.1 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 V3.1 or Gemini 2.5 Pro?
Gemini 2.5 Pro supports 1m tokens, while DeepSeek V3.1 supports 64k 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 V3.1 or Gemini 2.5 Pro?
DeepSeek V3.1 lists $0.27/1M input and $1/1M 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 $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 V3.1 or Gemini 2.5 Pro open source?
DeepSeek V3.1 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 V3.1 or Gemini 2.5 Pro?
Both DeepSeek V3.1 and Gemini 2.5 Pro expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek V3.1 or Gemini 2.5 Pro?
Both DeepSeek V3.1 and Gemini 2.5 Pro expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run DeepSeek V3.1 and Gemini 2.5 Pro?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. 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: 2026-06-29. Data sourced from public model cards and provider documentation.