Claude Opus 4.8 vs GPT-5.6 Luna
At a Glance
| Compare | Claude Opus 4.8Anthropic |  GPT-5.6 LunaOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | $1.20Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,000K | 1,050K |
| Model facts checked | Aug 29, 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 | Claude Opus 4.8 | GPT-5.6 Luna |
|---|---|---|
| ARC-AGI-1verified-v1-613c385b0c93 · verified_score · leader | 92.50100% of row best · percent · Claude Opus 4.8 (Max) | 90.6798% of row best · percent · GPT-5.6 Luna 2026-07-30 (Max) |
| ARC-AGI-2verified-v2-be40992860f9 · verified_score · leader | 72.08100% of row best · percent · Claude Opus 4.8 (High) | 59.5883% of row best · percent · GPT-5.6 Luna 2026-07-30 (Max) |
| LMArena Document Arenadocument-2026-09-13-0ebb411abf00 · arena_rating · statistical tie | 1,464.07100% of row best · rating · claude-opus-4-8; 95% CI [1457.10320334, 1471.03216030]; votes 12128; rank 19 | 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-2026-10-15-0ebb411abf00 · arena_rating · leader | 1,453.90100% of row best · rating · claude-opus-4-8; 95% CI [1450.05936004, 1457.74353427]; votes 65172; rank 47 | 1,430.6698% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1426.38028671, 1434.93119450]; votes 41109; rank 93 |
| LMArena Vision Arenavision-2026-10-09-0ebb411abf00 · arena_rating · leader | 1,288.52100% of row best · rating · claude-opus-4-8; 95% CI [1282.01202676, 1295.03051235]; votes 18852; rank 29 | 1,258.2498% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1250.82216689, 1265.66350676]; votes 11086; rank 58 |
| LiveBench2026-06-25-90f8d44da965 · 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 |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 80.70100% of row best · points · Claude Opus 4.8 (max effort) · 46,601 output tokens / case | 78.8398% of row best · points · GPT-5.6 Luna (ultra) · 19,621 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 5 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.
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 | 1,000K | 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 | Anthropic (Standard), Deepinfra (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Claude Opus 4.8 Capabilities
GPT-5.6 Luna Capabilities
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Claude Opus 4.8Anthropic | vs | cross-developer peersimage, text | |
Claude Opus 4.8Anthropic | vs | Grok 4.3xAI | 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 tokens and GPT-5.6 Luna supports 1,050,000 tokens.
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 6 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 sourced 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?+
Both models have the same sourced maximum output: 128,000 tokens each.
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.