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Claude Opus 5.5 vs GPT-5.6 Terra

Claude Opus 5.5 leads GPT-5.6 Terra on coding, 88.8 to 63.9 on the Vibe Coding Index, and GPT-5.6 Terra costs 1.7x less per output token ($12.00 vs $20.00 per million). Updated October 22, 2026; 93 benchmarks have a result for both.

Replace Claude Opus 5.5 Replace GPT-5.6 Terra
Claude Opus 5.5Anthropic
Coding
88.8
Overall
87.1
Output $/M
$20.00
90
GPT-5.6 TerraOpenAI
Coding
63.9
Overall
68.1
Output $/M
$12.00
3
vs boards won of 93 shared

Claude Opus 5.5 or GPT-5.6 Terra: which to pick

Pick Claude Opus 5.5 for coding: it scores 88.8 on the Vibe Coding Index against 63.9 for GPT-5.6 Terra, though on 5 benchmarks against 6.

Pick GPT-5.6 Terra for output tokens 1.7x cheaper ($12.00 vs $20.00 per million) and a larger context window (1.05M against 1M tokens).

Model AClaude Opus 5.5Anthropic88.8/1005 independent benchmarksNo cost per solved taskModel BGPT-5.6 TerraOpenAI63.9/1006 independent benchmarks$0.42 per solved task
Shared results
93
Claude Opus 5.5 only
16
GPT-5.6 Terra only
34
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Claude Opus 5.5 88.8 from 5 benchmarks, GPT-5.6 Terra 63.9 from 6 benchmarks.
Widest category
Web design, 19.9 points apart on average over 8 shared benchmarks.
Widest single gap
Terminal-Bench 4 (Vals AI): Claude Opus 5.5 65.2% against GPT-5.6 Terra 22.7%, 56.6 points apart on a 0 to 100 scale.
Output price
Claude Opus 5.5 $20.00 against GPT-5.6 Terra $12.00 per million output tokens, 1.7x apart.
Context window
Claude Opus 5.5 takes 1M tokens, GPT-5.6 Terra 1.05M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryClaude Opus 5.5BarsGPT-5.6 TerraTies
Coding190
Intelligence20
Agentic work121
Reasoning111
Math60
Knowledge70
Web design80
General251
All shared boards903
CodingLike-for-likeSix of these boards feed the Vibe Coding Index190
Coding benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
LiveBench coding96.3%86.2%Claude Opus 5.5
LMArena WebDev18071522Claude Opus 5.5
AA Coding IndexNot measured76.7Only GPT-5.6 Terra measured
LiveCodeBench (Vals AI)Not measured85.9%Only GPT-5.6 Terra measured
SWE-bench Verified (Vals AI)Not measured95.4%Only GPT-5.6 Terra measured
Terminal-Bench 4 (Vals AI)65.2%22.7%Claude Opus 5.5
Vibe Code Bench (Vals AI)90.3%74.6%Claude Opus 5.5
Code migration (Vals AI)66.7%47.8%Claude Opus 5.5
ProgramBench (Vals AI)18.5%0.5%Claude Opus 5.5
IOI (Vals AI)95.1%87.6%Claude Opus 5.5
Terminal-Bench 2.1 (Vals AI)87.6%77.5%Claude Opus 5.5
SWE-bench Multimodal (Vals AI)70.2%Not measuredOnly Claude Opus 5.5 measured
Vibe Code Bench 1-100 (Vals AI)30.4%14.8%Claude Opus 5.5
SkillsBench (Vals AI)Not measured58.9%Only GPT-5.6 Terra measured
CyberBench (Vals AI)74.6%72.1%Claude Opus 5.5
SciCode66.9%55.0%Claude Opus 5.5
Terminal-Bench Hard (Artificial Analysis run)Not measured62.9%Only GPT-5.6 Terra measured
FrontierCode54.6%41.3%Claude Opus 5.5
FrontierSWE62.3%Not measuredOnly Claude Opus 5.5 measured
WeirdMLNot measured78.3%Only GPT-5.6 Terra measured
MirrorCode77.4%Not measuredOnly Claude Opus 5.5 measured
ALE-Bench2147.41951.4Claude Opus 5.5
LMArena Text: coding15451485Claude Opus 5.5
LMArena Text: software and IT15381482Claude Opus 5.5
LMArena WebDev: React18551513Claude Opus 5.5
LMArena WebDev: HTML17531526Claude Opus 5.5
LMArena WebDev: from an image17531541Claude Opus 5.5
SciCode18.5%10.8%Claude Opus 5.5
Neither model has a result on the other 11 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
AA Intelligence Index57.642.1Claude Opus 5.5
Epoch Capabilities Index168.8160.1Claude Opus 5.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index121
Agentic work benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
AA Agentic IndexNot measured43.2Only GPT-5.6 Terra measured
Design Arena: agents building web apps13161120Claude Opus 5.5
Design Arena: agents building full-stack appsNot measured1061Only GPT-5.6 Terra measured
Design Arena: agents building mobile apps13081111Claude Opus 5.5
Design Arena: agents building Android appsNot measured1062Only GPT-5.6 Terra measured
Design Arena: agents building gamesNot measured1083Only GPT-5.6 Terra measured
Design Arena: HTML slidesNot measured1058Only GPT-5.6 Terra measured
Design Arena: PowerPoint slidesNot measured1105Only GPT-5.6 Terra measured
Design Arena: spreadsheets1368Not measuredOnly Claude Opus 5.5 measured
LiveBench research85.0%60.4%Claude Opus 5.5
LiveBench browser80.0%84.0%GPT-5.6 Terra
LiveBench long agentic85.0%48.4%Claude Opus 5.5
SREBench (Vals AI)33.6%9.5%Claude Opus 5.5
Finance Agent (Vals AI)58.6%54.4%Claude Opus 5.5
Tax Agent Bench (Vals AI)45.6%30.0%Claude Opus 5.5
Legal Research Bench (Vals AI)50.5%41.4%Claude Opus 5.5
Harvey's Legal Agent Benchmark (Vals AI)9.2%Not measuredOnly Claude Opus 5.5 measured
Excel Modeling Benchmark (Vals AI)75.9%66.2%Claude Opus 5.5
BioMysteryBench (Vals AI)79.3%Not measuredOnly Claude Opus 5.5 measured
Terminal-Bench-Science (Vals AI)47.1%10.0%Claude Opus 5.5
Vals Science Index58.7%Not measuredOnly Claude Opus 5.5 measured
Time Horizon Index (Vals AI)91.3%Not measuredOnly Claude Opus 5.5 measured
CUA-bench (Vals AI)14.0%Not measuredOnly Claude Opus 5.5 measured
Robo-bench (Vals AI)14.6%Not measuredOnly Claude Opus 5.5 measured
τ²-Bench TelecomNot measured86.3%Only GPT-5.6 Terra measured
APEX-Agents73.5%58.2%Claude Opus 5.5
Remote Labor Index21.3%Not measuredOnly Claude Opus 5.5 measured
BALROGNot measured53.2%Only GPT-5.6 Terra measured
SpireClimb3.3%Not measuredOnly Claude Opus 5.5 measured
Vending-Bench 2$9,235$7,343Claude Opus 5.5
LMArena Search1213Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 10 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index111
Reasoning benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
GPQA DiamondNot measured90.9%Only GPT-5.6 Terra measured
LiveBench reasoning92.2%90.6%Claude Opus 5.5
MysteryMechanism (Vals AI)49.5%Not measuredOnly Claude Opus 5.5 measured
MMMU Pro (Vals AI)Not measured86.5%Only GPT-5.6 Terra measured
GPQA Diamond (Artificial Analysis run)Not measured92.5%Only GPT-5.6 Terra measured
Humanity's Last Exam (Artificial Analysis run)61.4%42.9%Claude Opus 5.5
GPQA Diamond (Epoch AI run)90.6%93.3%GPT-5.6 Terra
ARC-AGI98.5%96.5%Claude Opus 5.5
ARC-AGI-293.3%83.9%Claude Opus 5.5
SimpleBenchNot measured48.9%Only GPT-5.6 Terra measured
Chess puzzles67.0%47.0%Claude Opus 5.5
Mystery game puzzles71.0%35.0%Claude Opus 5.5
EBR-bench71.4%Not measuredOnly Claude Opus 5.5 measured
LMArena Text: hard prompts15361469Claude Opus 5.5
LMArena Text: expert15491496Claude Opus 5.5
LMArena Text: hard prompts in English15351465Claude Opus 5.5
LMArena Vision: diagrams13451295Claude Opus 5.5
LMArena Vision: homework13621312Claude Opus 5.5
Kagi LLM BenchmarkNot measured51.3%Only GPT-5.6 Terra measured
Neither model has a result on the other 3 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index60
Math benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
OTIS Mock AIME100.0%99.7%Claude Opus 5.5
FrontierMath, tiers 1 to 391.2%86.0%Claude Opus 5.5
FrontierMath, tier 495.0%70.7%Claude Opus 5.5
ProofBench100.0%74.0%Claude Opus 5.5
LMArena Text: math15181465Claude Opus 5.5
LMArena Text: mathematical work15161478Claude Opus 5.5
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
Knowledge benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
MMLU ProNot measured86.7%Only GPT-5.6 Terra measured
LegalBench (Vals AI)Not measured85.1%Only GPT-5.6 Terra measured
CorpFin (Vals AI)Not measured65.3%Only GPT-5.6 Terra measured
TaxEval (Vals AI)Not measured76.2%Only GPT-5.6 Terra measured
MortgageTax (Vals AI)Not measured67.3%Only GPT-5.6 Terra measured
MedCode (Vals AI)49.8%43.4%Claude Opus 5.5
SimpleQA Verified72.2%43.2%Claude Opus 5.5
LMArena Text: science15241452Claude Opus 5.5
LMArena Text: medicine and healthcare15091430Claude Opus 5.5
LMArena Text: legal and government15181461Claude Opus 5.5
LMArena Text: business and finance14941448Claude Opus 5.5
FACTS Benchmark Suite65.3%52.0%Claude Opus 5.5
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
Design Arena: websites13351268Claude Opus 5.5
Design Arena: UI components13851256Claude Opus 5.5
Design Arena: game dev14611229Claude Opus 5.5
Design Arena: data viz13971240Claude Opus 5.5
Design Arena: 3D14761134Claude Opus 5.5
Design Arena: all code categories13901241Claude Opus 5.5
Design Arena: SVG13291219Claude Opus 5.5
Design Arena: ASCII artNot measured1177Only GPT-5.6 Terra measured
LiveBench design86.9%76.0%Claude Opus 5.5
GeneralLike-for-likePublic boards, shown as published, kept out of the index251
General benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
Vals Index70.2%Not measuredOnly Claude Opus 5.5 measured
LiveBench data analysis80.3%79.3%Claude Opus 5.5
Public Benefits Bench (Vals AI)70.6%62.4%Claude Opus 5.5
SAGE (Vals AI)45.8%47.0%GPT-5.6 Terra
MedScribe (Vals AI)91.4%82.9%Claude Opus 5.5
Vals Multimodal IndexNot measured65.1%Only GPT-5.6 Terra measured
IFBenchNot measured71.2%Only GPT-5.6 Terra measured
Long context reasoning (AA-LCR)84.7%83.0%Claude Opus 5.5
DTBench98.9%93.3%Claude Opus 5.5
LMCA68.2%54.9%Claude Opus 5.5
Surface Evolver BenchNot measured83.8%Only GPT-5.6 Terra measured
Furniture assembly83.3%54.2%Claude Opus 5.5
LMArena Text15161448Claude Opus 5.5
LMArena Text: instruction following15371456Claude Opus 5.5
LMArena Text: creative writing15351413Claude Opus 5.5
LMArena Vision13211271Claude Opus 5.5
LMArena Text: English15191449Claude Opus 5.5
LMArena Text: non-English15071441Claude Opus 5.5
LMArena Text: Chinese15821514Claude Opus 5.5
LMArena Text: GermanNot measured1460Only GPT-5.6 Terra measured
LMArena Text: Japanese15341454Claude Opus 5.5
LMArena Text: KoreanNot measured1427Only GPT-5.6 Terra measured
LMArena Text: French14931466Claude Opus 5.5
LMArena Text: Spanish15081451Claude Opus 5.5
LMArena Text: Russian15171452Claude Opus 5.5
LMArena Text: PolishNot measured1446Only GPT-5.6 Terra measured
LMArena Text: multi-turn15011453Claude Opus 5.5
LMArena Text: longer queries15321454Claude Opus 5.5
LMArena Text: writing and language15321431Claude Opus 5.5
LMArena Text: entertainment and media15151418Claude Opus 5.5
LMArena Vision: English13171272Claude Opus 5.5
LMArena Vision: ChineseNot measured1337Only GPT-5.6 Terra measured
LMArena Vision: reading text13261278Claude Opus 5.5
LMArena Vision: humorNot measured1236Only GPT-5.6 Terra measured
LMArena Vision: creative writing13201248Claude Opus 5.5
LMArena DocumentNot measured1472Only GPT-5.6 Terra measured
Neither model has a result on the other 10 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 5.5 and GPT-5.6 Terra
BenchmarkClaude Opus 5.5GPT-5.6 TerraLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Claude Opus 5.5$0.014Fits in one request
GPT-5.6 Terra$0.0080Fits in one request

GPT-5.6 Terra costs less

Repository review

50K input + 3K output tokens

Claude Opus 5.5$0.260Fits in one request
GPT-5.6 Terra$0.136Fits in one request

GPT-5.6 Terra costs less

Agent loop

200K input + 10K output tokens

Claude Opus 5.5$1.00Fits in one request
GPT-5.6 Terra$0.520Fits in one request

GPT-5.6 Terra costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Claude Opus 5.5 and GPT-5.6 Terra
SpecificationAnthropic Claude Opus 5.5OpenAI GPT-5.6 Terra
MakerAnthropicOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedSeptember 22, 2026July 9, 2026
Context window1M1.05M
Input $/Mtok$4.00$2.00
Output $/Mtok$20.00$12.00
Coding index88.863.9
Independent measurements behind it56
Cost per solved taskNot computable$0.42
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 5.5 better than GPT-5.6 Terra for coding?

Pick Claude Opus 5.5 for coding: it scores 88.8 on the Vibe Coding Index against 63.9 for GPT-5.6 Terra, though on 5 benchmarks against 6. Pick GPT-5.6 Terra for output tokens 1.7x cheaper ($12.00 vs $20.00 per million) and a larger context window (1.05M against 1M tokens).

Which is cheaper, Claude Opus 5.5 or GPT-5.6 Terra?

Claude Opus 5.5 costs $4.00 per million input tokens and $20.00 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Cost per solved task is known for GPT-5.6 Terra only.

Which has the bigger context window, Claude Opus 5.5 or GPT-5.6 Terra?

GPT-5.6 Terra, with 1.05M tokens against 1M.

How many benchmarks is the Claude Opus 5.5 vs GPT-5.6 Terra comparison based on?

93 benchmarks have a result for both models as of October 22, 2026. Claude Opus 5.5's coding index rests on 5 independent benchmarks and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.

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