GPT-5.2 vs GPT-5.6 Luna
At a Glance
| Compare |  GPT-5.2OpenAI | GPT-5.6 LunaOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #33 of 4649.3 score · 3/3 sources · complete | #31 of 4652.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #28 of 44$0.161 per LiveBench case | #6 of 44$0.026 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #34 of 3844.0 score · 3/3 sources · complete | #4 of 3864.8 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.75Openai ↗ · Sep 3, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $14.00Openai ↗ · Sep 3, 2026 | $1.20Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 400K | 1,050K |
| Model facts checked | Sep 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
| Benchmark | GPT-5.2 | GPT-5.6 Luna |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 86.1795% of row best · percent · GPT-5.2 (XHigh) | 90.67100% of row best · percent · GPT-5.6 Luna 2026-07-30 (Max) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 52.9189% of row best · percent · GPT-5.2 (XHigh) | 59.58100% of row best · percent · GPT-5.6 Luna 2026-07-30 (Max) |
| LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · leader | 1,401.1296% of row best · rating · gpt-5.2; 95% CI [1395.19508120, 1407.03614053]; votes 28212; rank 44 | 1,456.92100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1448.72029252, 1465.11301394]; votes 5766; rank 23 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,412.4399% of row best · rating · gpt-5.2; 95% CI [1409.21136225, 1415.65317114]; votes 78967; rank 125 | 1,429.89100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.11549148, 1434.66063238]; votes 28547; rank 86 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,229.4498% of row best · rating · gpt-5.2; 95% CI [1223.25930119, 1235.62760198]; votes 18997; rank 68 | 1,258.53100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1250.17799393, 1266.87249678]; votes 7793; rank 48 |
| LiveBench2026-06-25 · overall · leader | 79.21100% of row best · percent · gpt-5.2-2025-12-11-high · 11,512 output tokens / case | 77.0597% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 78.0892% of row best · points · GPT-5.2 · 4,266 output tokens / case | 84.47100% of row best · points · GPT-5.6 Luna (ultra) · 17,605 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark win | 5 benchmark winsOverall lead |
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
| Field | GPT-5.2 | GPT-5.6 Luna |
|---|---|---|
| Developer | OpenAI | OpenAI |
| Family | Gpt 5 2 | Gpt 5 6 |
| Model | GPT-5.2 | GPT-5.6 Luna |
| Version | GPT-5.2 | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | 2025-12-11 | Unknown |
| Knowledge cutoff | 2025-08-31 | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 400K | 1,050K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Openai (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, tools |
GPT-5.2 Capabilities
GPT-5.6 Luna Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-5.2 vs GPT-5.6 Luna FAQs
Is GPT-5.2 or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.2 and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.2 or GPT-5.6 Luna?+
GPT-5.2 is $1.75 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. GPT-5.2 is $14.00 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, GPT-5.2 or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. GPT-5.2 supports 400K and GPT-5.6 Luna supports 1,050K.
Which performs better in benchmarks, GPT-5.2 or GPT-5.6 Luna?+
GPT-5.6 Luna leads the current overall benchmark count. The result uses 6 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can GPT-5.2 or GPT-5.6 Luna be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.2 is not marked open weight; GPT-5.6 Luna is not marked open weight.
Can GPT-5.2 and GPT-5.6 Luna understand images?+
GPT-5.2 is 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, GPT-5.2 or GPT-5.6 Luna?+
Neither has a larger sourced maximum output. GPT-5.2 is 128K and GPT-5.6 Luna is 128K.
Do GPT-5.2 and GPT-5.6 Luna support reasoning and tool use?+
GPT-5.2: reasoning, tool calling, and image input. GPT-5.6 Luna: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.2 or GPT-5.6 Luna?+
GPT-5.2 has 2 sourced provider routes; GPT-5.6 Luna has 2, a tie.
Which offers better value, GPT-5.2 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.