Claude Opus 4.8 vs GPT-5.6 Luna
Benchmark Performance
Available Benchmarks
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna |
|---|---|---|
| LMArena Document Arenadocument-2026-07-30-011508720696 · arena_rating · statistical tie | 1,468.90100% of row best · rating · claude-opus-4-8; 95% CI [1460.58009276, 1477.22476172]; votes 8193; rank 16 | 1,462.30100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1449.02133383, 1475.58666708]; votes 1888; rank 18 |
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,451.98100% of row best · rating · claude-opus-4-8; 95% CI [1447.69484580, 1456.25837678]; votes 49123; rank 38 | 1,430.1498% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.14784950, 1435.13857729]; votes 24433; rank 82 |
| LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · leader | 1,288.81100% of row best · rating · claude-opus-4-8; 95% CI [1281.38044914, 1296.24567110]; votes 13980; rank 23 | 1,254.3097% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1245.05722338, 1263.53552757]; votes 5857; rank 45 |
| LiveBench2026-06-25 · overall · leader | 81.18100% of row best · percent · claude-opus-4-8-max-effort · 24,171 output tokens / case | 77.0595% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case |
| ToneBench2026-08-14-10-task · overall_score · leader | 88.09100% of row best · points · Claude Opus 4.8 (max effort) · 13,476 output tokens / case | 83.5995% of row best · points · GPT-5.6 Luna (xhigh) · 6,093 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 4 benchmark winsOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Claude Opus 4.8 | GPT-5.6 Luna |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 8 | Gpt 5 6 |
| Model | Claude Opus 4.8 | GPT-5.6 Luna |
| Version | Claude Opus 4.8 | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | 2026-05-28 | Unknown |
| Knowledge cutoff | 2026-01-01 | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000,000 | 1,050,000 |
| 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 | Anthropic (Standard), Deepinfra (Standard) | OpenAI (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
14 comparable fields · 7 material differences · Pair passes the primary-source comparison gate
Claude Opus 4.8 Capabilities
GPT-5.6 Luna Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
Claude Opus 4.8Anthropic | vs | Claude Opus 5Anthropic | family variantsimage, text |
Claude Fable 5Anthropic | vs | Claude Opus 4.8Anthropic | family variantsimage, text |
Claude Mythos 5.1Anthropic | vs | Claude Opus 4.8Anthropic | family variantsimage, text |
Claude Opus 4.8Anthropic | vs | Claude Sonnet 5Anthropic | family variantsimage, text |
GPT-4.1OpenAI | vs | GPT-5.6 LunaOpenAI | family variantsimage, text |
GPT-4.1 MiniOpenAI | vs | GPT-5.6 LunaOpenAI | family variantsimage, text |
GPT-4oOpenAI | vs | GPT-5.6 LunaOpenAI | family variantsimage, text |
GPT-4.1 NanoOpenAI | vs | GPT-5.6 LunaOpenAI | family variantsimage, text |
 Mistral-Small-4-119B-2603Mistral AI | vs | GPT-5.6 LunaOpenAI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Claude Opus 4.8Anthropic | cross-developer peersimage, text |
Claude Fable 5Anthropic | vs | GPT-5.6 LunaOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | GPT-5.6 LunaOpenAI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.8 vs GPT-5.6 Luna FAQs
Is Claude Opus 4.8 or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.8 and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.8 or GPT-5.6 Luna?+
Claude Opus 4.8 is $5.00 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. Claude Opus 4.8 is $25.00 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, Claude Opus 4.8 or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. Claude Opus 4.8 supports 1,000,000 and GPT-5.6 Luna supports 1,050,000.
Which performs better in benchmarks, Claude Opus 4.8 or GPT-5.6 Luna?+
Claude Opus 4.8 leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can Claude Opus 4.8 or GPT-5.6 Luna be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Opus 4.8 is not marked open weight; GPT-5.6 Luna is not marked open weight.
Can Claude Opus 4.8 and GPT-5.6 Luna understand images?+
Claude Opus 4.8 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, Claude Opus 4.8 or GPT-5.6 Luna?+
Neither has a larger sourced maximum output. Claude Opus 4.8 is 128,000 and GPT-5.6 Luna is 128,000.
Do Claude Opus 4.8 and GPT-5.6 Luna support reasoning and tool use?+
Claude Opus 4.8: 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, Claude Opus 4.8 or GPT-5.6 Luna?+
Claude Opus 4.8 has 2 sourced provider routes; GPT-5.6 Luna has 2, a tie.
Which offers better value, Claude Opus 4.8 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.