Claude Opus 4.6 vs GPT-5.6 Terra

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#12 of 4676.2 score · 3/3 sources · complete#13 of 4674.9 score · 3/3 sources · complete
CostLower is better · Published-token output estimate#33 of 44$0.241 per LiveBench case#35 of 44$0.266 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#19 of 3853.2 score · 3/3 sources · complete#23 of 3851.5 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokens$5.00Anthropic · Sep 3, 2026$2.00Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokens$25.00Anthropic · Sep 3, 2026$12.00Openai · Sep 3, 2026
Context windowMaximum documented tokens1,000K1,050K
Model facts checkedSep 3, 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

All benchmark results →
BenchmarkClaude Opus 4.6GPT-5.6 Terra
ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader94.0097% of row best · percent · Claude Opus 4.6 (120K, High)96.50100% of row best · percent · GPT-5.6 Terra (Max)
ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader69.1782% of row best · percent · Claude Opus 4.6 (120K, High)83.90100% of row best · percent · GPT-5.6 Terra (Max)
LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · leader1,507.05100% of row best · rating · claude-opus-4-6; 95% CI [1501.18336037, 1512.92390326]; votes 41260; rank 41,471.5898% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1463.31179298, 1479.85049651]; votes 5787; rank 13
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,497.54100% of row best · rating · claude-opus-4-6; 95% CI [1494.14263199, 1500.94441396]; votes 75878; rank 51,446.2297% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1441.41414497, 1451.02563541]; votes 28119; rank 52
LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader1,311.54100% of row best · rating · claude-opus-4-6; 95% CI [1305.06259741, 1318.02420613]; votes 25140; rank 91,269.9097% of row best · rating · gpt-5.6-terra-xhigh; 95% CI [1261.54272100, 1278.25950723]; votes 7726; rank 36
LiveBench2026-06-25 · overall · leader78.6996% of row best · percent · claude-opus-4-6-thinking-auto-high-effort · 9,651 output tokens / case82.31100% of row best · percent · gpt-5.6-terra-max · 22,145 output tokens / case
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified84.2197% of row best · points · Claude Opus 4.6 · 13,556 output tokens / case86.39100% of row best · points · GPT-5.6 Terra (ultra) · 11,336 output tokens / case
Overall ResultCounted from the protocol-matched rows above3 benchmark winsNo overall winner3 benchmark winsNo overall winner

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

FieldClaude Opus 4.6GPT-5.6 Terra
DeveloperAnthropicOpenAI
FamilyClaude 4Gpt 5 6
ModelClaude Opus 4.6GPT-5.6 Terra
VersionClaude Opus 4.6GPT-5.6 Terra
Lifecycleactiveactive
Released2026-02-05Unknown
Knowledge cutoff2025-05-012026-02-16
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,000K1,050K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessAnthropic (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolschat, generation, reasoning, tools

Claude Opus 4.6 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers1
Canonical IDanthropic/claude-opus-4-6

GPT-5.6 Terra Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-5.6-terra

Primary Evidence

Sources and Freshness

Questions

Claude Opus 4.6 vs GPT-5.6 Terra FAQs

Is Claude Opus 4.6 or GPT-5.6 Terra better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.6 and GPT-5.6 Terra, 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-5.6 Terra?+

Claude Opus 4.6 is $5.00 and GPT-5.6 Terra is $2.00 per million tokens, so GPT-5.6 Terra is cheaper on this metric. Claude Opus 4.6 is $25.00 and GPT-5.6 Terra is $12.00 per million tokens, so GPT-5.6 Terra is cheaper on this metric.

Which has a larger context window, Claude Opus 4.6 or GPT-5.6 Terra?+

GPT-5.6 Terra has the larger sourced context window. Claude Opus 4.6 supports 1,000K and GPT-5.6 Terra supports 1,050K.

Which performs better in benchmarks, Claude Opus 4.6 or GPT-5.6 Terra?+

There is no overall benchmark winner: The verified common benchmarks do not produce a majority winner.

Can Claude Opus 4.6 or GPT-5.6 Terra be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Claude Opus 4.6 is not marked open weight; GPT-5.6 Terra is not marked open weight.

Can Claude Opus 4.6 and GPT-5.6 Terra understand images?+

Claude Opus 4.6 is documented with image input; GPT-5.6 Terra is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Claude Opus 4.6 or GPT-5.6 Terra?+

Neither has a larger sourced maximum output. Claude Opus 4.6 is 128K and GPT-5.6 Terra is 128K.

Do Claude Opus 4.6 and GPT-5.6 Terra support reasoning and tool use?+

Claude Opus 4.6: reasoning, tool calling, and image input. GPT-5.6 Terra: 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-5.6 Terra?+

Claude Opus 4.6 has 1 sourced provider route; GPT-5.6 Terra has 2, so GPT-5.6 Terra has broader tracked availability.

Which offers better value, Claude Opus 4.6 or GPT-5.6 Terra?+

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.

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