DeepSeek V3.2 vs GPT-5.5
DeepSeek V3.2 (2025) and GPT-5.5 (2026) are frontier reasoning models from DeepSeek and OpenAI. DeepSeek V3.2 ships a 160k-token context window, while GPT-5.5 ships a 1.05m-token context window. On SWE-bench Verified, GPT-5.5 leads by 12.6 pts. On pricing, DeepSeek V3.2 costs $0.25/1M input tokens; GPT-5.5 ranges from $5 to $8/1M input tokens by tier. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
GPT-5.5 fits 7x more tokens; pick it for long-context work and DeepSeek V3.2 for tighter calls.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.2 | GPT-5.5 |
|---|---|---|
| Best for | provider-routed production | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 160k | 1.05m |
| Cheapest output | $0.38/1M tokens | $30/1M tokens |
| Provider routes | 7 tracked | 4 tracked |
| Shared benchmarks | 2 shared | SWE-bench Verified leader |
Decision tradeoffs
- DeepSeek V3.2 has the lower cheapest tracked output price at $0.38/1M tokens.
- DeepSeek V3.2 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags DeepSeek V3.2 for Coding, RAG, and Agents.
- GPT-5.5 holds a shared-benchmark lead on SWE-bench Verified, ahead by 12.6 points.
- GPT-5.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.5 uniquely exposes Vision, Multimodal, and Reasoning in local model data.
- Local decision data tags GPT-5.5 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 V3.2
$296
Cheapest tracked route/tier: OpenRouter
GPT-5.5
$11,500
Cheapest tracked route/tier: OpenAI API 0-272K input tokens
Estimated monthly gap: $11,204. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Vercel AI Gateway, and AWS Bedrock; start route-level A/B tests there.
- GPT-5.5 is $29.62/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.5 adds Vision, Multimodal, and Reasoning in local capability data.
- Provider overlap exists on AWS Bedrock, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- DeepSeek V3.2 is $29.62/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Reasoning before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2026-04-23 |
| Context window | 160k | 1.05m |
| Parameters | 671B | — |
| Architecture | Decoder Only | Decoder Only |
| License | MIT(OSI) | Proprietary |
| Openness | Open source | Proprietary |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | 2025-12 |
Pricing and availability
| Pricing attribute | DeepSeek V3.2 | GPT-5.5 |
|---|---|---|
| Input price | $0.25/1M tokens |
|
| Output price | $0.38/1M tokens |
|
| Providers |
Capabilities
| Capability | DeepSeek V3.2 | GPT-5.5 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | 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 V3.2 | GPT-5.5 |
|---|---|---|
| SWE-bench Verified | 70.0 | 82.6 |
| Google-Proof Q&A | 84.0 | 93.6 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek V3.2 at 70 and GPT-5.5 at 82.6, with GPT-5.5 ahead by 12.6 points; Google-Proof Q&A has DeepSeek V3.2 at 84 and GPT-5.5 at 93.6, with GPT-5.5 ahead by 9.6 points. The largest visible gap is 12.6 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: GPT-5.5, multimodal input: GPT-5.5, reasoning mode: GPT-5.5, function calling: GPT-5.5, and tool use: GPT-5.5. 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 $0.25/1M input and $0.38/1M output tokens on the cheapest tracked provider, while GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M output. A 70/30 input-output blend puts DeepSeek V3.2 lower by about $12.21 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 7 providers versus 4, 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 GPT-5.5 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 GPT-5.5?
GPT-5.5 supports 1.05m 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, DeepSeek V3.2 or GPT-5.5?
DeepSeek V3.2 lists $0.25/1M input and $0.38/1M output tokens on the cheapest tracked provider. GPT-5.5 lists tiered pricing: 0-272K input tokens is $5/1M input and $30/1M output; 272K+ input tokens is $8/1M input and $36/1M 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 V3.2 or GPT-5.5 open source?
DeepSeek V3.2 is listed under MIT. GPT-5.5 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 GPT-5.5?
GPT-5.5 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek V3.2 or GPT-5.5?
GPT-5.5 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 GPT-5.5?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, OpenRouter, and Microsoft Foundry. GPT-5.5 is available on OpenAI API, OpenRouter, Vercel AI Gateway, and AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.