DeepSeek V4 Pro 0813 vs GPT-5.6 Luna

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#20 of 4665.0 score · 3/3 sources · complete#31 of 4652.7 score · 3/3 sources · complete
CostLower is better · Published-token output estimate#20 of 44$0.094 per LiveBench case#6 of 44$0.026 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#14 of 3857.4 score · 3/3 sources · complete#4 of 3864.8 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.30Deepinfra · Sep 23, 2026$0.20Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokens$2.60Deepinfra · Sep 23, 2026$1.20Openai · Sep 3, 2026
Context windowMaximum documented tokens1,049K1,050K
Model facts checkedSep 3, 2026View model evidence →Aug 28, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
BenchmarkDeepSeek-V4-Pro-0813GPT-5.6 Luna
ARC-AGI-1verified-v1-354e4ceb2669 · verified_score · leader90.50100% of row best · percent · DeepSeek V4 Pro 0813 (Low)90.67100% of row best · percent · GPT-5.6 Luna 2026-07-30 (Max)
ARC-AGI-2verified-v2-9d422cc0fd9e · verified_score · leader61.25100% of row best · percent · DeepSeek V4 Pro 0813 (Max)59.5897% of row best · percent · GPT-5.6 Luna 2026-07-30 (Max)
LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader4.14100% of row best · score · DeepSeek V4 Pro (High) (0813); 95% CI [3.35150819, 4.93122185]; sessions 47602; observations 4132769; rank 16-0.4496% of row best · score · GPT 5.6 Luna (xHigh); 95% CI [-1.27310649, 0.39719069]; sessions 29186; observations 2307801; rank 27
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,443.95100% of row best · rating · deepseek-v4-pro-high-20260813; 95% CI [1437.16050212, 1450.73220317]; votes 9008; rank 581,429.8999% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.11549148, 1434.66063238]; votes 28547; rank 86
LiveBench2026-06-25 · overall · leader81.91100% of row best · percent · deepseek-v4-pro-0813 · 36,339 output tokens / case77.0594% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified84.68100% of row best · points · DeepSeek V4 Pro 0813 (max) · 18,462 output tokens / case84.47100% of row best · points · GPT-5.6 Luna (ultra) · 17,605 output tokens / case
Overall ResultCounted from the protocol-matched rows above4 benchmark winsOverall lead1 benchmark win

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Side-by-Side Facts

FieldDeepSeek-V4-Pro-0813GPT-5.6 Luna
DeveloperDeepSeekOpenAI
FamilyDeepseek V4 ProGpt 5 6
ModelDeepSeek-V4-Pro-0813GPT-5.6 Luna
VersionDeepSeek-V4-Pro-0813GPT-5.6 Luna
Lifecycleactiveactive
Released2026-08-13Unknown
Knowledge cutoffUnknown2026-02-16
Input modalitiesTextText, Image
Output modalitiesTextText
Context window1,049K1,050K
Total parameters1.6TUnknown
Active parameters49BUnknown
LicensemitUnknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessDeepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard)Openai (Standard), Openrouter (Standard)
Capabilitiesagents, chat, coding, generation, reasoning, toolschat, generation, reasoning, tools

DeepSeek V4 Pro 0813 Capabilities

agentschatcodinggenerationreasoningtools
Serving providers3
Canonical IDdeepseek-ai/DeepSeek-V4-Pro-0813

GPT-5.6 Luna Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-5.6-luna

Primary Evidence

Sources and Freshness

Questions

DeepSeek V4 Pro 0813 vs GPT-5.6 Luna FAQs

Is DeepSeek V4 Pro 0813 or GPT-5.6 Luna better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro 0813 and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?+

DeepSeek V4 Pro 0813 is $1.30 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. DeepSeek V4 Pro 0813 is $2.60 and GPT-5.6 Luna is $1.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric.

Which has a larger context window, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?+

GPT-5.6 Luna has the larger sourced context window. DeepSeek V4 Pro 0813 supports 1,049K and GPT-5.6 Luna supports 1,050K.

Which performs better in benchmarks, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?+

DeepSeek V4 Pro 0813 leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.

Can DeepSeek V4 Pro 0813 or GPT-5.6 Luna be self-hosted?+

DeepSeek V4 Pro 0813 is the only model in this pair currently marked as self-hostable. DeepSeek V4 Pro 0813 is open weight; GPT-5.6 Luna is not marked open weight.

Can DeepSeek V4 Pro 0813 and GPT-5.6 Luna understand images?+

DeepSeek V4 Pro 0813 is not documented with image input; GPT-5.6 Luna is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?+

Neither has a larger sourced maximum output. DeepSeek V4 Pro 0813 is — and GPT-5.6 Luna is 128K.

Do DeepSeek V4 Pro 0813 and GPT-5.6 Luna support reasoning and tool use?+

DeepSeek V4 Pro 0813: reasoning and tool calling. GPT-5.6 Luna: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?+

DeepSeek V4 Pro 0813 has 3 sourced provider routes; GPT-5.6 Luna has 2, so DeepSeek V4 Pro 0813 has broader tracked availability.

Which offers better value, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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