GPT-5.6 Luna vs DeepSeek V4 Pro
GPT-5.6 Luna and DeepSeek V4 Pro both promise frontier-level engineering with a million-token context, but the economics are worlds apart. Luna brings OpenAI's managed tool ecosystem and a stronger published coding profile; V4 Pro brings 1.6T parameters, MIT weights, 384K output, extreme cache economics and a serious long-context story.
Model cards side by side
| Specification | GPT-5.6 Luna | DeepSeek V4 Pro |
|---|---|---|
| Provider / release | OpenAI · July 2026 GA | DeepSeek · April 24, 2026 preview/API release |
| API model ID | gpt-5.6-luna | deepseek-v4-pro |
| Architecture | Proprietary | MoE · hybrid Compressed Sparse Attention / Heavily Compressed Attention |
| Parameters | Not disclosed | 1.6T total / 49B active |
| Context window | 1.05M tokens | 1M tokens |
| Maximum output | 128K tokens | 384K tokens |
| Reasoning modes | High tier in the GPT-5.6 family | Non-thinking, Think High, Think Max |
| Input / output | Text and images / text | Text API / text |
| Tools | Hosted web, file, code, shell, computer use, MCP and tool search | Tool calls, JSON output, OpenAI-compatible and Anthropic-compatible APIs |
| Weights / license | Closed, proprietary | Open weights, MIT |
| Concurrency shown in official docs | OpenAI tier limits | 500 |
DeepSeek's architectural claim is unusually specific: in a 1M-token setting, its model card says V4 Pro requires about 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. That does not make a 1.6T model easy to self-host, but it explains why the API can offer a million-token window at a very low price.
Benchmarks: Luna leads the comparable coding lane, DeepSeek fights back on breadth
DeepSeek's strongest public comparison numbers use V4 Pro Max, the highest reasoning-effort mode of the same API model. Luna's numbers are OpenAI's official GPT-5.6 scorecard. Rows marked with different benchmark versions or missing provider results should not be converted into a single overall score.
| Evaluation | Luna | V4 Pro Max | Interpretation |
|---|---|---|---|
| SWE-Bench Pro | 62.7% | 55.4% | Luna +7.3 · different harness lanes |
| GPQA Diamond | 92.3% | 90.1% | Luna +2.2 · both high capability |
| BrowseComp | 83.3% | 83.4% | Near tie · provider-reported |
| Toolathlon | 53.4% | 51.8% | Luna +1.6 · close |
| Terminal-Bench | 84.7% on 2.1 | 67.9% on 2.0 | Version mismatch; no direct win claim |
| MCP Atlas public | Not published by OpenAI | 73.6% | Unmatched evidence |
| Humanity's Last Exam with tools | Not in Luna's official table | 48.2% | Unmatched evidence |
| LiveCodeBench | Not published in the Luna table | 93.5% | DeepSeek-only evidence |
Radar: four direct evidence points
● Luna ● V4 Pro Max
The radar uses the four rows where both models have a public number in the cited comparison set. It is a visual aid, not a new benchmark.
- Luna has a clearer lead on the selected SWE Pro and GPQA rows.
- BrowseComp is effectively tied in the published figures.
- DeepSeek's advantage appears outside this four-point polygon: open weights, caching, output budget and 1M-context economics.
Long context and reasoning modes
Luna: 1.05M context with a 128K ceiling
GPT-5.6 Luna uses the same 1.05M context window as the other GPT-5.6 tiers. OpenAI's long-context table shows that a maximum window is not the same as perfect retrieval: Luna's MRCR v2 score is 41.3% in both the 256K–512K and 512K–1M ranges, while its GraphWalks BFS F1 is 51.2 at 1M.
For most normal code agents, 128K output is ample. For workflows that generate very large reports, patches or intermediate artifacts, it is a hard limit.
V4 Pro: 1M context and 384K output
DeepSeek's official API and model card list a 1M context and a 384K maximum output. V4 Pro supports non-thinking, Think High and Think Max, so a developer can trade answer speed for deeper search. Its hybrid attention design is intended to keep long prompts economically viable.
The extra headroom is meaningful for full-repository analysis, large research logs and long agent trajectories. It is still wise to compact context: a larger window does not remove the need for retrieval, prioritization and tool-state management.
| Long-task requirement | Luna | V4 Pro |
|---|---|---|
| Hold an entire large codebase | 1.05M context | 1M context |
| Generate a very long artifact | 128K maximum output | 384K maximum output |
| Choose reasoning effort | GPT-5.6 tier/effort controls | Non-thinking, High, Max |
| Keep repeated system context cheap | 90% cached-input discount | Extremely low cache-hit price |
Pricing: DeepSeek changes the cost model
| Per 1M tokens | GPT-5.6 Luna | DeepSeek V4 Pro |
|---|---|---|
| Fresh input / cache miss | $1.00 | $0.435 |
| Cached input / cache hit | $0.10 | $0.003625 |
| Output | $6.00 | $0.87 |
| 10M fresh input + 1M output | $16.00 | $5.22 |
| 10M cached input + 1M output | $7.00 | $0.90625 |
DeepSeek is cheaper on every token class in the official rate card. The difference is especially dramatic for cache-heavy agents: V4 Pro's $0.003625/M cache-hit price is roughly 28 times below Luna's $0.10/M cached input. The practical caveat is operational: DeepSeek lists a 500-request concurrency limit, while OpenAI limits depend on account tier, and the cheapest price is not useful if retries, latency or human review erase the quality advantage.
Licensing, ecosystem and deployment
| Decision factor | Luna | V4 Pro |
|---|---|---|
| Managed API maturity | Strong OpenAI ecosystem | Official APIs plus compatibility layers |
| Self-hosting | Not available | MIT open weights, but 1.6T is infrastructure-heavy |
| Image input | Supported | Not listed in the cited text API/model card |
| Tool integration | Hosted tools, MCP, tool search | Tool calls and OpenAI/Anthropic-compatible endpoints |
| Output price | $6/M | $0.87/M |
| Data/control posture | Provider-managed | API or self-hosted path, subject to operations |
“Open weights” should not be mistaken for “cheap to operate.” A 1.6T mixture-of-experts model can reduce active compute per token, but serving, memory, quantization, networking, batching and reliability are still serious engineering work. The MIT license is a strategic advantage for sovereign deployments, fine-tuning and auditability—not an automatic cost guarantee.
Which model should you use?
Choose GPT-5.6 Luna if…
- You want a managed service with image input and OpenAI's hosted web, file, code, shell, computer-use and MCP tools.
- Your primary workload is repository coding and the cited Luna scores matter: 62.7% SWE Pro and 84.7% Terminal-Bench 2.1.
- You want a straightforward API route to other GPT-5.6 tiers when confidence falls.
- You prefer platform integration and safety operations over weight-level control.
Choose DeepSeek V4 Pro if…
- API cost is the first-order constraint, especially for output-heavy or cache-heavy agents.
- You need MIT weights, self-hosting or an air-gapped deployment option.
- You need 384K maximum output and a million-token context for long research or code artifacts.
- You can evaluate Think High/Max, concurrency, latency and tool reliability in your own environment.
Compare both models on real code
Run the same repository prompt, tool schema and acceptance tests rather than trusting a leaderboard headline.
Try both in CodingFleet →Final verdict
GPT-5.6 Luna wins the managed capability contest. Its public scorecard shows stronger results on the selected coding rows, and its image/tool ecosystem is easier to adopt for a production application.
DeepSeek V4 Pro wins the economics and control contest. It costs a fraction as much, supports an enormous output budget, offers a 1M context, exposes open MIT weights and makes repeated-context agents unusually cheap.
For a startup optimizing unit economics, V4 Pro is difficult to ignore. For a team optimizing integration risk and tool-rich quality, Luna is the safer first deployment. The strongest architecture is often a router that uses both.
Sources and methodology
- OpenAI — GPT-5.6 and GPT-5.6 Luna API documentation. Luna pricing, features and benchmark table.
- DeepSeek V4 preview release and DeepSeek official API pricing.
- DeepSeek V4 Pro model card. Parameters, context, modes, license and Pro Max comparison scores.
- All benchmark rows retain their source labels and modes. Provider-reported results can differ because of harness, prompt, context and reasoning-budget choices. The radar and bar chart are visual aids, not a statistically normalized leaderboard.