Claude Opus 4.6 vs GPT-5.4 Mini
At a Glance
| Compare | Claude Opus 4.6Anthropic | GPT-5.4 MiniOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #12 of 4676.2 score · 3/3 sources · complete | #38 of 4620.2 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #33 of 44$0.241 per LiveBench case | #32 of 44$0.214 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #19 of 3853.2 score · 3/3 sources · complete | #38 of 3826.5 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | $0.75Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | $4.50Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,000K | 400K |
| Model facts checked | Sep 3, 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 4.6 | GPT-5.4 Mini |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 94.00100% of row best · percent · Claude Opus 4.6 (120K, High) | 63.6768% of row best · percent · GPT-5.4 Mini (XHigh) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 69.17100% of row best · percent · Claude Opus 4.6 (120K, High) | 18.9027% of row best · percent · GPT-5.4 Mini (XHigh) |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,497.54100% of row best · rating · claude-opus-4-6; 95% CI [1494.14263199, 1500.94441396]; votes 75878; rank 5 | 1,412.1194% of row best · rating · gpt-5.4-mini-high; 95% CI [1408.29499732, 1415.92580718]; votes 59387; rank 127 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,311.54100% of row best · rating · claude-opus-4-6; 95% CI [1305.06259741, 1318.02420613]; votes 25140; rank 9 | 1,245.0995% of row best · rating · gpt-5.4-mini-high; 95% CI [1238.67164332, 1251.50821847]; votes 24041; rank 59 |
| LiveBench2026-06-25 · overall · leader | 78.69100% of row best · percent · claude-opus-4-6-thinking-auto-high-effort · 9,651 output tokens / case | 69.5388% of row best · percent · gpt-5.4-mini-xhigh · 47,448 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 84.21100% of row best · points · Claude Opus 4.6 · 13,556 output tokens / case | 82.1698% of row best · points · GPT-5.4 mini (high) · 9,924 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 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 4.6 | GPT-5.4 Mini |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 | Gpt 5 4 |
| Model | Claude Opus 4.6 | GPT-5.4 Mini |
| Version | Claude Opus 4.6 | GPT-5.4 Mini |
| Lifecycle | active | active |
| Released | 2026-02-05 | 2026-03-17 |
| Knowledge cutoff | 2025-05-01 | 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) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
Claude Opus 4.6 Capabilities
GPT-5.4 Mini Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.6 vs GPT-5.4 Mini FAQs
Is Claude Opus 4.6 or GPT-5.4 Mini better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.6 and GPT-5.4 Mini, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.6 or GPT-5.4 Mini?+
Claude Opus 4.6 is $5.00 and GPT-5.4 Mini is $0.75 per million tokens, so GPT-5.4 Mini is cheaper on this metric. Claude Opus 4.6 is $25.00 and GPT-5.4 Mini is $4.50 per million tokens, so GPT-5.4 Mini is cheaper on this metric.
Which has a larger context window, Claude Opus 4.6 or GPT-5.4 Mini?+
Claude Opus 4.6 has the larger sourced context window. Claude Opus 4.6 supports 1,000K and GPT-5.4 Mini supports 400K.
Which performs better in benchmarks, Claude Opus 4.6 or GPT-5.4 Mini?+
Claude Opus 4.6 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 4.6 or GPT-5.4 Mini be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Opus 4.6 is not marked open weight; GPT-5.4 Mini is not marked open weight.
Can Claude Opus 4.6 and GPT-5.4 Mini understand images?+
Claude Opus 4.6 is documented with image input; GPT-5.4 Mini is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 4.6 or GPT-5.4 Mini?+
Neither has a larger sourced maximum output. Claude Opus 4.6 is 128K and GPT-5.4 Mini is 128K.
Do Claude Opus 4.6 and GPT-5.4 Mini support reasoning and tool use?+
Claude Opus 4.6: reasoning, tool calling, and image input. GPT-5.4 Mini: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Opus 4.6 or GPT-5.4 Mini?+
Claude Opus 4.6 has 1 sourced provider route; GPT-5.4 Mini has 2, so GPT-5.4 Mini has broader tracked availability.
Which offers better value, Claude Opus 4.6 or GPT-5.4 Mini?+
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.