DeepSeek V4 Flash vs GPT-5.5 Pro
DeepSeek V4 Flash (2026) and GPT-5.5 Pro (2026) are frontier-tier reasoning models from DeepSeek and OpenAI. DeepSeek V4 Flash ships a 1M-token context window, while GPT-5.5 Pro ships a 1.05M-token context window. On SWE-bench Verified, GPT-5.5 Pro leads by 3.6 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek V4 Flash is safer overall; choose GPT-5.5 Pro when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | DeepSeek V4 Flash | GPT-5.5 Pro |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and long-context analysis | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1M | 1.05M |
| Cheapest output | $0.12/1M tokens | $180/1M tokens |
| Provider routes | 5 tracked | 3 tracked |
| Shared benchmarks | 8 shared | SWE-bench Verified leader |
Decision tradeoffs
- DeepSeek V4 Flash holds a shared-benchmark lead on LiveCodeBench, ahead by 0.6 points.
- DeepSeek V4 Flash has the lower cheapest tracked output price at $0.12/1M tokens.
- DeepSeek V4 Flash has broader tracked provider coverage for fallback and route flexibility.
- Local decision data tags DeepSeek V4 Flash for Coding, RAG, and Agents.
- GPT-5.5 Pro holds a shared-benchmark lead on SWE-bench Verified, ahead by 3.6 points.
- GPT-5.5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.5 Pro uniquely exposes Vision, Multimodal, and Code execution in local model data.
- Local decision data tags GPT-5.5 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 V4 Flash
$76.26
Cheapest tracked route/tier: OpenRouter
GPT-5.5 Pro
$69,000
Cheapest tracked route/tier: OpenAI API <=272K tokens (standard)
Estimated monthly gap: $68,924. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.5 Pro is $180/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.5 Pro adds Vision, Multimodal, and Code execution in local capability data.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- DeepSeek V4 Flash is $180/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Code execution before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-24 | 2026-04-23 |
| Context window | 1M | 1.05M |
| Parameters | 284B | — |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Proprietary |
| Openness | Open source | Proprietary |
| Weights | Available | Not released |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | - | 2025-12 |
Pricing and availability
| Pricing attribute | DeepSeek V4 Flash | GPT-5.5 Pro |
|---|---|---|
| Input price |
| $30/1M tokens |
| Output price |
| $180/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V4 Flash | GPT-5.5 Pro |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| JSON / Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V4 Flash | GPT-5.5 Pro |
|---|---|---|
| SWE-bench Verified | 79.0 | 82.6 |
| SWE-bench Pro | 52.6 | 58.6 |
| Google-Proof Q&A | 88.1 | 93.6 |
| LiveCodeBench | 91.6 | 91.0 |
| Terminal-Bench 2.0 | 56.9 | 82.7 |
| GeneBench-Pro | 2.4 | 20.5 |
| Humanity's Last Exam — With Tools | 45.1 | 52.2 |
| MCP-Atlas | 69.0 | 75.3 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek V4 Flash at 79 and GPT-5.5 Pro at 82.6, with GPT-5.5 Pro ahead by 3.6 points; SWE-bench Pro has DeepSeek V4 Flash at 52.6 and GPT-5.5 Pro at 58.6, with GPT-5.5 Pro ahead by 6 points; Google-Proof Q&A has DeepSeek V4 Flash at 88.1 and GPT-5.5 Pro at 93.6, with GPT-5.5 Pro ahead by 5.5 points. The largest visible gap is 6 points on SWE-bench 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 vision: GPT-5.5 Pro, multimodal input: GPT-5.5 Pro, and code execution: GPT-5.5 Pro. Both models share reasoning mode, JSON / Tool use, and structured outputs, 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 V4 Flash lists tiered pricing: Off-peak is $0.22/1M input and $0.66/1M output; Peak is $0.44/1M input and $1.32/1M output, while GPT-5.5 Pro lists $30/1M input and $180/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V4 Flash lower by about $74.92 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 5 providers versus 3, so concentration risk also matters.
Choose DeepSeek V4 Flash when provider fit, lower cheapest-tier input-token cost, and broader provider choice are central to the workload. Choose GPT-5.5 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 V4 Flash or GPT-5.5 Pro?
GPT-5.5 Pro supports 1.05M tokens, while DeepSeek V4 Flash supports 1M 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 V4 Flash or GPT-5.5 Pro?
DeepSeek V4 Flash lists tiered pricing: Off-peak is $0.22/1M input and $0.66/1M output; Peak is $0.44/1M input and $1.32/1M output. GPT-5.5 Pro lists $30/1M input and $180/1M output tokens on the cheapest tracked provider. 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 V4 Flash or GPT-5.5 Pro open source?
DeepSeek V4 Flash is listed under MIT. GPT-5.5 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 V4 Flash or GPT-5.5 Pro?
GPT-5.5 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek V4 Flash or GPT-5.5 Pro?
GPT-5.5 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 V4 Flash and GPT-5.5 Pro?
DeepSeek V4 Flash is available on DeepSeek Platform, OpenRouter, Microsoft Foundry, Vercel AI Gateway, and Novita AI. GPT-5.5 Pro is available on OpenAI API, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-10-08. Data sourced from public model cards and provider documentation.