DeepSeek R1 vs DeepSeek V3.2
DeepSeek R1 (2025) and DeepSeek V3.2 (2025) are frontier reasoning models from DeepSeek. DeepSeek R1 ships a 128k-token context window, while DeepSeek V3.2 ships a 160k-token context window. On SWE-bench Verified, DeepSeek V3.2 leads by 20.8 pts. On pricing, DeepSeek R1 costs $0.10/1M input tokens versus $0.25/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek R1 is ~152% cheaper at $0.10/1M; pay for DeepSeek V3.2 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 | DeepSeek V3.2 |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 128k | 160k |
| Cheapest output | $0.30/1M tokens | $0.38/1M tokens |
| Provider routes | 14 tracked | 7 tracked |
| Shared benchmarks | 2 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.
- DeepSeek R1 uniquely exposes Reasoning in local model data.
- Local decision data tags DeepSeek R1 for Coding, RAG, and Agents.
- DeepSeek V3.2 holds a shared-benchmark lead on SWE-bench Verified, ahead by 20.8 points.
- DeepSeek V3.2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags DeepSeek V3.2 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
DeepSeek V3.2
$296
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $141. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, NVIDIA NIM, and AWS Bedrock; start route-level A/B tests there.
- DeepSeek V3.2 is $0.08/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Reasoning before moving production traffic.
- Provider overlap exists on OpenRouter, Fireworks AI, and NVIDIA NIM; start route-level A/B tests there.
- DeepSeek R1 is $0.08/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- DeepSeek R1 adds Reasoning in local capability data.
Specs
Pricing and availability
| Pricing attribute | DeepSeek R1 | DeepSeek V3.2 |
|---|---|---|
| Input price | $0.10/1M tokens | $0.25/1M tokens |
| Output price | $0.30/1M tokens | $0.38/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 | DeepSeek V3.2 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek R1 | DeepSeek V3.2 |
|---|---|---|
| SWE-bench Verified | 49.2 | 70.0 |
| Google-Proof Q&A | 71.5 | 84.0 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek R1 at 49.2 and DeepSeek V3.2 at 70, with DeepSeek V3.2 ahead by 20.8 points; Google-Proof Q&A has DeepSeek R1 at 71.5 and DeepSeek V3.2 at 84, with DeepSeek V3.2 ahead by 12.5 points. The largest visible gap is 20.8 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 reasoning mode: DeepSeek R1. 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 R1 lists $0.10/1M input and $0.30/1M output tokens on the cheapest tracked provider, while DeepSeek V3.2 lists $0.25/1M input and $0.38/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 lower by about $0.13 per million blended tokens. Availability is 14 providers versus 7, 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 DeepSeek V3.2 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 DeepSeek V3.2?
DeepSeek V3.2 supports 160k 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 DeepSeek V3.2?
DeepSeek R1 is cheaper on tracked token pricing. DeepSeek R1 costs $0.10/1M input and $0.30/1M output tokens. DeepSeek V3.2 costs $0.25/1M input and $0.38/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 or DeepSeek V3.2 open source?
DeepSeek R1 is listed under MIT. DeepSeek V3.2 is listed under MIT. 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 reasoning mode, DeepSeek R1 or DeepSeek V3.2?
DeepSeek R1 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, DeepSeek R1 or DeepSeek V3.2?
Both DeepSeek R1 and DeepSeek V3.2 expose structured outputs. 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.
Where can I run DeepSeek R1 and DeepSeek V3.2?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, OpenRouter, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.