Claude Opus 4.8 vs GPT-4.1
At a Glance
| Compare | Claude Opus 4.8Anthropic |  GPT-4.1OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #18 of 4670.1 score · 3/3 sources · complete | #44 of 463.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 2.5–35.8 |
| CostLower is better · Published-token output estimate | #42 of 44$0.604 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #35 of 3840.5 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | $1.00Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | $4.00Openrouter ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,000K | 1,048K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 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-4.1 |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 92.50100% of row best · percent · Claude Opus 4.8 (Max) | 5.506% of row best · percent · GPT-4.1 |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 72.08100% of row best · percent · Claude Opus 4.8 (High) | 0.421% of row best · percent · GPT-4.1 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,452.66100% of row best · rating · claude-opus-4-8; 95% CI [1448.52490690, 1456.79817098]; votes 53446; rank 40 | 1,382.7795% of row best · rating · gpt-4.1-2025-04-14; 95% CI [1379.06025569, 1386.47352582]; votes 49941; rank 157 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,288.23100% of row best · rating · claude-opus-4-8; 95% CI [1281.27991800, 1295.18871241]; votes 15801; rank 25 | 1,210.0394% of row best · rating · gpt-4.1-2025-04-14; 95% CI [1203.34343280, 1216.70707848]; votes 41012; rank 74 |
| Overall ResultCounted from the protocol-matched rows above | 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.
Side-by-Side Facts
| Field | Claude Opus 4.8 | GPT-4.1 |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 8 | Gpt 4 1 |
| Model | Claude Opus 4.8 | GPT-4.1 |
| Version | Claude Opus 4.8 | GPT-4.1 |
| Lifecycle | active | active |
| Released | 2026-05-28 | Unknown |
| Knowledge cutoff | 2026-01-01 | 2024-06-01 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 1,048K |
| 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, tools |
Claude Opus 4.8 Capabilities
GPT-4.1 Capabilities
Related Comparisons
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|---|---|---|---|
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Claude Opus 4.8Anthropic | vs | Grok 4.6xAI | cross-developer peersimage, text |
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Claude Opus 4.8Anthropic | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Claude Opus 4.8Anthropic | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Claude Opus 4.8Anthropic | vs |  Hy4 previewTencent | cross-developer peerstext |
Claude Opus 4.8Anthropic | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.8 vs GPT-4.1 FAQs
Is Claude Opus 4.8 or GPT-4.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.8 and GPT-4.1, 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-4.1?+
Claude Opus 4.8 is $5.00 and GPT-4.1 is $1.00 per million tokens, so GPT-4.1 is cheaper on this metric. Claude Opus 4.8 is $25.00 and GPT-4.1 is $4.00 per million tokens, so GPT-4.1 is cheaper on this metric.
Which has a larger context window, Claude Opus 4.8 or GPT-4.1?+
GPT-4.1 has the larger sourced context window. Claude Opus 4.8 supports 1,000K and GPT-4.1 supports 1,048K.
Which performs better in benchmarks, Claude Opus 4.8 or GPT-4.1?+
Claude Opus 4.8 leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Claude Opus 4.8 or GPT-4.1 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Opus 4.8 is not marked open weight; GPT-4.1 is not marked open weight.
Can Claude Opus 4.8 and GPT-4.1 understand images?+
Claude Opus 4.8 is documented with image input; GPT-4.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 4.8 or GPT-4.1?+
Claude Opus 4.8 has the larger sourced maximum output: Claude Opus 4.8 supports 128K and GPT-4.1 supports 33K output tokens.
Do Claude Opus 4.8 and GPT-4.1 support reasoning and tool use?+
Claude Opus 4.8: reasoning, tool calling, and image input. GPT-4.1: 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-4.1?+
Claude Opus 4.8 has 2 sourced provider routes; GPT-4.1 has 2, a tie.
Which offers better value, Claude Opus 4.8 or GPT-4.1?+
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.