DeepSeek V3.2 vs Gemini 2.5 Pro
DeepSeek V3.2 (2025) and Gemini 2.5 Pro (2025) are general-purpose language models from DeepSeek and Google DeepMind. DeepSeek V3.2 ships a 160K-token context window, while Gemini 2.5 Pro ships a 1M-token context window. On SWE-bench Verified, DeepSeek V3.2 leads by 6.8 pts. On pricing, DeepSeek V3.2 costs $0.26/1M input tokens versus $1.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
DeepSeek V3.2 is ~383% cheaper at $0.26/1M; pay for Gemini 2.5 Pro only for coding workflow support.
Specs
| Released | 2025-01-01 | 2025-06-17 |
| Context window | 160K | 1M |
| Parameters | 671B | — |
| Architecture | decoder only | decoder only |
| License | Open Source | Proprietary |
| Knowledge cutoff | - | 2025-01 |
Pricing and availability
| DeepSeek V3.2 | Gemini 2.5 Pro | |
|---|---|---|
| Input price | $0.26/1M tokens | $1.25/1M tokens |
| Output price | $0.42/1M tokens | $10/1M tokens |
| Providers |
Capabilities
| DeepSeek V3.2 | Gemini 2.5 Pro | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Pro |
|---|---|---|
| SWE-bench Verified | 70.0 | 63.2 |
| Google-Proof Q&A | 84.0 | 86.4 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek V3.2 at 70 and Gemini 2.5 Pro at 63.2, with DeepSeek V3.2 ahead by 6.8 points; Google-Proof Q&A has DeepSeek V3.2 at 84 and Gemini 2.5 Pro at 86.4, with Gemini 2.5 Pro ahead by 2.4 points. The largest visible gap is 6.8 points on SWE-bench Verified, 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 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 V3.2 lists $0.26/1M input and $0.42/1M output tokens, while Gemini 2.5 Pro lists $1.25/1M input and $10/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V3.2 lower by about $3.57 per million blended tokens. Availability is 4 providers versus 3, so concentration risk also matters.
Choose DeepSeek V3.2 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 V3.2 or Gemini 2.5 Pro?
Gemini 2.5 Pro supports 1M tokens, while DeepSeek V3.2 supports 160K 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 V3.2 or Gemini 2.5 Pro?
DeepSeek V3.2 is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.26/1M input and $0.42/1M output tokens. Gemini 2.5 Pro costs $1.25/1M input and $10/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Gemini 2.5 Pro open source?
DeepSeek V3.2 is listed under Open Source. 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 V3.2 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 V3.2 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 V3.2 and Gemini 2.5 Pro?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.