DeepSeek V4 Pro vs GPT-5.6 Sol
GPT-5.6 Sol and DeepSeek V4 Pro compare OpenAI's closed July 2026 GA frontier route with DeepSeek's open-weight long-context text model. Sol is built for OpenAI-hosted agentic, multimodal, and cyber workloads with 1.05M context and July launch-table evidence. DeepSeek V4 Pro offers downloadable weights, very low API pricing, and 1M text-only context for teams that prioritize cost and deployment control.
Pick GPT-5.6 Sol when you need OpenAI integration, multimodal input, max reasoning effort, and the strongest first-party agent/cyber/science launch rows. Pick DeepSeek V4 Pro when open weights, self-hosting or weight inspection, and extremely low token prices matter more than closed-service frontier features. Keep modality discipline: DeepSeek V4 Pro is text-only in local seed data.
Decision scorecard
Local evidence first| Signal | DeepSeek V4 Pro | GPT-5.6 Sol |
|---|---|---|
| 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 | $0.87/1M tokens | $30/1M tokens |
| Provider routes | 5 tracked | 2 tracked |
| Shared benchmarks | 4 shared | SWE-bench Pro leader |
Decision tradeoffs
- DeepSeek V4 Pro has the lower cheapest tracked output price at $0.87/1M tokens.
- DeepSeek V4 Pro has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek V4 Pro uniquely exposes Structured outputs in local model data.
- Local decision data tags DeepSeek V4 Pro for Coding, RAG, and Agents.
- GPT-5.6 Sol holds a shared-benchmark lead on SWE-bench Pro, ahead by 9.2 points.
- GPT-5.6 Sol has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.6 Sol uniquely exposes Vision, Multimodal, and Code execution in local model data.
- Local decision data tags GPT-5.6 Sol 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 Pro
$566
Cheapest tracked route/tier: DeepSeek Platform
GPT-5.6 Sol
$11,500
Cheapest tracked route/tier: OpenAI API 0-272K input tokens
Estimated monthly gap: $10,935. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GPT-5.6 Sol is $29.13/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- GPT-5.6 Sol adds Vision, Multimodal, and Code execution in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- DeepSeek V4 Pro is $29.13/1M 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.
- DeepSeek V4 Pro adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-24 | 2026-07-09 |
| Context window | 1m | 1.05m |
| Parameters | 1.6T | — |
| 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 | - | - |
Pricing and availability
| Pricing attribute | DeepSeek V4 Pro | GPT-5.6 Sol |
|---|---|---|
| Input price | $0.43/1M tokens |
|
| Output price | $0.87/1M tokens |
|
| Providers |
Capabilities
| Capability | DeepSeek V4 Pro | GPT-5.6 Sol |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V4 Pro | GPT-5.6 Sol |
|---|---|---|
| SWE-bench Pro | 55.4 | 64.6 |
| Google-Proof Q&A | 90.1 | 94.6 |
| GeneBench-Pro | 2.4 | 31.5 |
| BrowseComp | 83.4 | 90.4 |
Deep dive
Deployment model is the first split. GPT-5.6 Sol is a closed OpenAI-hosted frontier service across ChatGPT, Codex, and the API. DeepSeek V4 Pro is open-weight with Hugging Face checkpoint access and low-cost DeepSeek API pricing.
Cost can favor DeepSeek V4 Pro dramatically on token economics, but Sol adds multimodal input, OpenAI tool ecosystems, and July 2026 GA launch rows that text-only open-weight routes cannot match one-for-one.
Benchmark comparisons need harness labels and modality guards. Use OpenAI GA rows for Sol and DeepSeek-specific rows for V4 Pro. Do not compare image or multimodal tasks as if DeepSeek V4 Pro supports them.
For teams choosing between frontier closed service and open-weight efficiency, run route-level tests on coding agents, long-context retrieval, and total cost of ownership including hosting or provider fees.
FAQ
Which model is open weight?
DeepSeek V4 Pro is tracked as open-weight with a public Hugging Face checkpoint. GPT-5.6 Sol is proprietary and available through OpenAI-hosted routes only.
Which model is cheaper to run?
DeepSeek V4 Pro is generally much cheaper on tracked API pricing and can be self-hosted from open weights. GPT-5.6 Sol uses OpenAI frontier pricing with batch, cache, and long-context surcharges that should be modeled on your real prompt length.
Does DeepSeek V4 Pro support images?
No in the local seed. DeepSeek V4 Pro is tracked as text-only, while GPT-5.6 Sol supports multimodal input. Do not use Sol multimodal benchmark or capability claims as DeepSeek evidence.
Which model is stronger for coding agents?
Compare source-labeled coding rows and your own harness. GPT-5.6 Sol has July 2026 OpenAI GA agent and coding launch evidence. DeepSeek V4 Pro has strong cost-efficient coding rows in the seed, but the final choice depends on deployment control, modality needs, and acceptance-test quality.
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.