Claude Opus 5 vs GPT-5.2
At a Glance
| Compare | Claude Opus 5Anthropic | GPT-5.2OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #3 of 4694.1 score · 3/3 sources · complete | #33 of 4649.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #39 of 44$0.371 per LiveBench case | #28 of 44$0.161 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #13 of 3857.6 score · 3/3 sources · complete | #34 of 3844.0 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | $1.75Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | $14.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,000K | 400K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 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 5 | GPT-5.2 |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 97.50100% of row best · percent · Claude Opus 5 (Max) | 86.1788% of row best · percent · GPT-5.2 (XHigh) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 90.42100% of row best · percent · Claude Opus 5 (Max) | 52.9159% of row best · percent · GPT-5.2 (XHigh) |
| LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · leader | 1,516.27100% of row best · rating · claude-opus-5-high; 95% CI [1509.24512533, 1523.28765667]; votes 8794; rank 1 | 1,401.1292% of row best · rating · gpt-5.2; 95% CI [1395.19508120, 1407.03614053]; votes 28212; rank 44 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,504.92100% of row best · rating · claude-opus-5-high; 95% CI [1500.70174969, 1509.13092375]; votes 42617; rank 3 | 1,412.4394% of row best · rating · gpt-5.2; 95% CI [1409.21136225, 1415.65317114]; votes 78967; rank 125 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,321.21100% of row best · rating · claude-opus-5-high; 95% CI [1313.68410123, 1328.73941303]; votes 11599; rank 3 | 1,229.4493% of row best · rating · gpt-5.2; 95% CI [1223.25930119, 1235.62760198]; votes 18997; rank 68 |
| LiveBench2026-06-25 · overall · leader | 83.44100% of row best · percent · claude-opus-5-max-effort · 14,826 output tokens / case | 79.2195% of row best · percent · gpt-5.2-2025-12-11-high · 11,512 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 88.76100% of row best · points · Claude Opus 5 (max effort) · 9,446 output tokens / case | 78.0888% of row best · points · GPT-5.2 · 4,266 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 6 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 5 | GPT-5.2 |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 5 | Gpt 5 2 |
| Model | Claude Opus 5 | GPT-5.2 |
| Version | Claude Opus 5 | GPT-5.2 |
| Lifecycle | active | active |
| Released | 2026-07-24 | 2025-12-11 |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 400K |
| 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), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, structured_outputs, tools |
Claude Opus 5 Capabilities
GPT-5.2 Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Opus 5 vs GPT-5.2 FAQs
Is Claude Opus 5 or GPT-5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 5 and GPT-5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 5 or GPT-5.2?+
Claude Opus 5 is $5.00 and GPT-5.2 is $1.75 per million tokens, so GPT-5.2 is cheaper on this metric. Claude Opus 5 is $25.00 and GPT-5.2 is $14.00 per million tokens, so GPT-5.2 is cheaper on this metric.
Which has a larger context window, Claude Opus 5 or GPT-5.2?+
Claude Opus 5 has the larger sourced context window. Claude Opus 5 supports 1,000K and GPT-5.2 supports 400K.
Which performs better in benchmarks, Claude Opus 5 or GPT-5.2?+
Claude Opus 5 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 5 or GPT-5.2 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Opus 5 is not marked open weight; GPT-5.2 is not marked open weight.
Can Claude Opus 5 and GPT-5.2 understand images?+
Claude Opus 5 is documented with image input; GPT-5.2 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 5 or GPT-5.2?+
Neither has a larger sourced maximum output. Claude Opus 5 is 128K and GPT-5.2 is 128K.
Do Claude Opus 5 and GPT-5.2 support reasoning and tool use?+
Claude Opus 5: reasoning, tool calling, and image input. GPT-5.2: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Opus 5 or GPT-5.2?+
Claude Opus 5 has 3 sourced provider routes; GPT-5.2 has 2, so Claude Opus 5 has broader tracked availability.
Which offers better value, Claude Opus 5 or GPT-5.2?+
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.