Model comparison
DeepSeek V4 Pro (Max) vs GPT-5.6 Luna
Head-to-head evidence from 25 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro (Max) unranked (Not scored); GPT-5.6 Luna #22 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro (Max) and GPT-5.6 Luna share 25 comparable benchmark results. 4 of 8 categories are comparable. 23 results are unique to DeepSeek V4 Pro (Max); 16 to GPT-5.6 Luna.
Updated July 23, 2026- Shared results
- 25
- DeepSeek V4 Pro (Max) only
- 23
- GPT-5.6 Luna only
- 16
- Comparable categories
- 4 / 8
Treat this as a split decision. DeepSeek V4 Pro (Max) makes more sense if mathematics is the priority or you want the cheaper token bill; GPT-5.6 Luna is the better fit if knowledge is the priority.
Confidence note. This is a partial-evidence comparison with 25 shared benchmark results across 4 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V4 Pro (Max) and GPT-5.6 Luna finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GPT-5.6 Luna is also the more expensive model on tokens at $1.00 input / $6.00 output per 1M tokens, versus $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro (Max). That is roughly 6.9x on output cost alone.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V4 Pro (Max) | Δ | GPT-5.6 Luna |
|---|---|---|---|
| Knowledge | DeepSeek V4 Pro (Max)60.1 | Margin→ 32.2 | GPT-5.6 Luna92.3 |
| Math | DeepSeek V4 Pro (Max)95.2 | Margin← 21.6 | GPT-5.6 Luna73.6 |
| Agentic | DeepSeek V4 Pro (Max)74.5 | Margin→ 9.6 | GPT-5.6 Luna84.1 |
| Coding | DeepSeek V4 Pro (Max)70.9 | Margin← 8.2 | GPT-5.6 Luna62.7 |
| Multimodal | DeepSeek V4 Pro (Max)Not measured | MarginNo overlap | GPT-5.6 Luna78.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 67.9%B 84.7%Winner: GPT-5.6 LunaΔ 16.8Terminal-Bench 2.0: DeepSeek V4 Pro (Max) scored 67.9%; GPT-5.6 Luna scored 84.7%. GPT-5.6 Luna wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.4%B 62.7%Winner: GPT-5.6 LunaΔ 7.3SWE-bench Pro: DeepSeek V4 Pro (Max) scored 55.4%; GPT-5.6 Luna scored 62.7%. GPT-5.6 Luna wins this benchmark. - Source ↗
GPQA
KnowledgeA 90.1%B 92.3%Winner: GPT-5.6 LunaΔ 2.2GPQA: DeepSeek V4 Pro (Max) scored 90.1%; GPT-5.6 Luna scored 92.3%. GPT-5.6 Luna wins this benchmark. - Source ↗
BrowseComp
AgenticA 83.4%B 83.3%Winner: DeepSeek V4 Pro (Max)Δ 0.1BrowseComp: DeepSeek V4 Pro (Max) scored 83.4%; GPT-5.6 Luna scored 83.3%. DeepSeek V4 Pro (Max) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro (Max) | GPT-5.6 Luna | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | GPT-5.6 Luna$1 input / $6 output | DeepSeek V4 Pro (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Pro (Max)Not available | GPT-5.6 LunaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro (Max)Not available | GPT-5.6 LunaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro (Max)1M | GPT-5.6 Luna1M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.6 Luna wins21 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GPT-5.6 Luna | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 67.9% | 84.7% | GPT-5.6 Luna leads |
| BrowseCompSource | 83.4% | 83.3% | DeepSeek V4 Pro (Max) leads |
| HLE w/ toolsSource | 48.2% | — | Not comparable |
| MCP AtlasSource | 73.6% | — | Not comparable |
| GDPval-AASource | 1307 | 1584 | GPT-5.6 Luna leads |
| ToolathlonSource | 51.8% | 53.4% | GPT-5.6 Luna leads |
| AA Agentic IndexSource | 36.4% | 45.6% | GPT-5.6 Luna leads |
| APEX-Agents-AASource | 24.3% | 35.8% | GPT-5.6 Luna leads |
| τ²-bench resultsSource | 96.2% | — | Not comparable |
| GDPval-AASource | 40.4% | 54.2% | GPT-5.6 Luna leads |
| AA BriefcaseSource | 932 | — | Not comparable |
| AA EnterpriseOps-GymSource | 40.4% | — | Not comparable |
| AA Harvey LABSource | 84.4% | 87.9% | GPT-5.6 Luna leads |
| AA ITBenchSource | 38.3% | 40.3% | GPT-5.6 Luna leads |
| AA Tau3 BankingSource | 25.8% | 27.2% | GPT-5.6 Luna leads |
| terminalBenchHardSource | 46.2% | — | Not comparable |
| aaTerminalBench21Source | 64% | 80.9% | GPT-5.6 Luna leads |
| OSWorld 2.0Source | — | 45.6% | Not comparable |
| CyberGymSource | — | 77.9% | Not comparable |
| ExploitGymSource | — | 12.4% | Not comparable |
| AA AutomationBenchSource | — | 42.2% | Not comparable |
CodingDeepSeek V4 Pro (Max) wins11 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GPT-5.6 Luna | Result |
|---|---|---|---|
| CodeforcesSource | 3206.0 | — | Not comparable |
| SWE-bench VerifiedSource | 80.6% | — | Not comparable |
| SWE-bench ProSource | 55.4% | 62.7% | GPT-5.6 Luna leads |
| SWE MultilingualSource | 76.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 67.9% | 84.7% | GPT-5.6 Luna leads |
| Vibe Code BenchSource | 49.93% | — | Not comparable |
| AA Coding IndexSource | 59.4% | 71.5% | GPT-5.6 Luna leads |
| AA-SciCodeSource | 50.0% | 52.5% | GPT-5.6 Luna leads |
| deepSweSource | — | 67.2% | Not comparable |
| FrontierCode 1.1 ExtendedSource | — | 55.1% | Not comparable |
| cursorBench32Source | — | 61.1% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.6 Luna wins15 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GPT-5.6 Luna | Result |
|---|---|---|---|
| MMLU-ProSource | 87.5% | — | Not comparable |
| SimpleQASource | 57.9% | — | Not comparable |
| Chinese-SimpleQASource | 84.4% | — | Not comparable |
| GPQASource | 90.1% | 92.3% | GPT-5.6 Luna leads |
| GPQA-DSource | 90.1% | 92.3% | GPT-5.6 Luna leads |
| HLESource | 37.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 44.3% | 51.2% | GPT-5.6 Luna leads |
| AA-GPQA DiamondSource | 88.8% | 91.1% | GPT-5.6 Luna leads |
| AA-HLESource | 35.9% | 37.2% | GPT-5.6 Luna leads |
| AA-Omniscience IndexSource | -10.0% | -11.2% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience AccuracySource | 43.3% | 41.5% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 90.1% | GPT-5.6 Luna leads |
| AA Openness IndexSource | 50.0% | — | Not comparable |
| HealthBench ProfessionalSource | — | 55.7% | Not comparable |
| HealthBench HardSource | — | 32.0% | Not comparable |
MathDeepSeek V4 Pro (Max) wins7 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GPT-5.6 Luna | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 95.2% | — | Not comparable |
| IMOAnswerBenchSource | 89.8% | — | Not comparable |
| ApexSource | 38.3% | — | Not comparable |
| Apex ShortlistSource | 90.2% | — | Not comparable |
| FrontierMath (legacy)Source | — | 78.6% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 78.600% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 58.500% | Not comparable |
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GPT-5.6 Luna | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.5% | — | Not comparable |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Pro (Max) or GPT-5.6 Luna?
DeepSeek V4 Pro (Max) and GPT-5.6 Luna are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, DeepSeek V4 Pro (Max) or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for knowledge tasks in this comparison, averaging 92.3 versus 60.1. Inside this category, Artificial Analysis Intelligence Index is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Pro (Max) or GPT-5.6 Luna?
DeepSeek V4 Pro (Max) has the edge for coding in this comparison, averaging 70.9 versus 62.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V4 Pro (Max) or GPT-5.6 Luna?
DeepSeek V4 Pro (Max) has the edge for math in this comparison, averaging 95.2 versus 73.6. GPT-5.6 Luna stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, DeepSeek V4 Pro (Max) or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for agentic tasks in this comparison, averaging 84.1 versus 74.5. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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