Claude Opus 4.6 vs GPT-5.6 Terra

Updated September 24, 2026. Rank says GPT-5.6 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Terra has the higher public score estimate, 72.58 versus 63.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Claude Opus 4.6

Anthropic

63.64/100

Supported · Public rank #31

90% interval 51.6–75.6

Model B

GPT-5.6 Terra

OpenAI

72.58/100

Supported · Public rank #9

90% interval 68.5–76.7

Shared results: 10
Claude Opus 4.6 only: 24
GPT-5.6 Terra only: 22
Like-for-like categories: 3 / 8

Supported: Claude Opus 4.6 and GPT-5.6 TerraHow the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Terra
GPT-5.6 Terra leads on the public coding lane, 64.7 to 51.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Terra
GPT-5.6 Terra leads on the public agentic lane, 59.5 to 44.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Terra
GPT-5.6 Terra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Terra
GPT-5.6 Terra has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
GPT-5.6 Terra
GPT-5.6 Terra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

51.1Claude Opus 4.664.7GPT-5.6 Terra

Like-for-like · BenchAlign v5.7

GPT-5.6 Terra leads the like-for-like coding row, although the 90% intervals overlap.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

FrontierMath v2 (Tier 4)Math
Normalized gap 45.4
Claude Opus 4.6:22.900%
GPT-5.6 Terra:68.300%
Claude Opus 4.6 source GPT-5.6 Terra source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 44.2
Claude Opus 4.6:40.700%
GPT-5.6 Terra:84.900%
Claude Opus 4.6 source GPT-5.6 Terra source
SWE-bench ProCoding
Normalized gap 10.0
Claude Opus 4.6:53.4%
GPT-5.6 Terra:63.4%
Claude Opus 4.6 source GPT-5.6 Terra source
BrowseCompAgentic
Normalized gap 3.8
Claude Opus 4.6:83.7%
GPT-5.6 Terra:87.5%
Claude Opus 4.6 source GPT-5.6 Terra source
MMMU-ProMultimodal
Normalized gap 3.4
Claude Opus 4.6:77.3%
GPT-5.6 Terra:80.7%
Claude Opus 4.6 source GPT-5.6 Terra source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Like-for-like

Claude Opus 4.6: 44.3; Supported · #39/105
GPT-5.6 Terra: 59.5; Supported · #18/105
Basis: BenchAlign v5.7 lane · 10 vs 9 public rows
Reading: GPT-5.6 Terra leads · intervals overlap

Coding

Like-for-like

Claude Opus 4.6: 51.1; Supported · #36/135
GPT-5.6 Terra: 64.7; Supported · #8/135
Basis: BenchAlign v5.7 lane · 8 vs 8 public rows
Reading: GPT-5.6 Terra leads · intervals overlap

Multimodal

Like-for-like

Claude Opus 4.6: 59.6; #30/50
GPT-5.6 Terra: 77.2; #16/50
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: GPT-5.6 Terra leads

Knowledge

Directional only

Claude Opus 4.6: 58.7; Estimated · #37/158
GPT-5.6 Terra: 71.1; Supported · #9/158
Basis: BenchAlign v5.7 lane · 9 vs 8 public rows
Reading: Directional only

Instruction following

Directional only

Claude Opus 4.6: 51.0; #79/124
GPT-5.6 Terra: 85.7; #35/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.6: 68.1; Unranked · 2 rankable rows
GPT-5.6 Terra: 65.4; #12/19
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.6: Not ranked
GPT-5.6 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.6: 58.5; Unranked · 3 rankable rows
GPT-5.6 Terra: 96.8; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.6 and GPT-5.6 Terra
Category	Claude Opus 4.6	GPT-5.6 Terra	Basis	Reading
Agentic	44.3Supported · #39/105	59.5Supported · #18/105	Like-for-likeBenchAlign v5.7 lane · 10 vs 9 public rows	GPT-5.6 Terra leads · intervals overlap
Coding	51.1Supported · #36/135	64.7Supported · #8/135	Like-for-likeBenchAlign v5.7 lane · 8 vs 8 public rows	GPT-5.6 Terra leads · intervals overlap
Multimodal	59.6#30/50	77.2#16/50	Like-for-likeProvisional lane · 1 vs 1 weighted rows	GPT-5.6 Terra leads
Knowledge	58.7Estimated · #37/158	71.1Supported · #9/158	Directional onlyBenchAlign v5.7 lane · 9 vs 8 public rows	Directional only
Instruction following	51.0#79/124	85.7#35/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	68.1Unranked · 2 rankable rows	65.4#12/19	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	58.5Unranked · 3 rankable rows	96.8Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Opus 4.6: $0.0175; Fits in one request
GPT-5.6 Terra: $0.008; Fits in one request

GPT-5.6 Terra has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.6: $0.325; Fits in one request
GPT-5.6 Terra: $0.136; Fits in one request

GPT-5.6 Terra has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Opus 4.6: $1.35; Fits in one request; Cached input priced at the published list-input rate
GPT-5.6 Terra: $0.2; Fits in one request

GPT-5.6 Terra has the lower modeled cost

Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationClaude Opus 4.6GPT-5.6 Terra

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 4.6

GPT-5.6 Terra

1.05M

OpenAI model catalog

API model ID

Claude Opus 4.6

Not sourced

GPT-5.6 Terra

gpt-5.6-terra

OpenAI model catalog

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Opus 4.6

Not published

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Documented inputs

Claude Opus 4.6

Not sourced

GPT-5.6 Terra

text, image

OpenAI model catalog

Documented outputs

Claude Opus 4.6

Not sourced

GPT-5.6 Terra

text

OpenAI model catalog

Provider availability

Claude Opus 4.6

Not sourced

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Claude Opus 4.6

Non-Reasoning

GPT-5.6 Terra

Reasoning

Weight access

Claude Opus 4.6

Proprietary

GPT-5.6 Terra

Proprietary

License

Claude Opus 4.6

Proprietary

GPT-5.6 Terra

Proprietary

Release date

Claude Opus 4.6

2026-02-01

GPT-5.6 Terra

2026-07-09

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: GPT-5.6 Terra has the higher public score estimate, 72.58 versus 63.64, but the 90% score intervals overlap.
Workload cost: Repository review: $0.325 vs $0.136. Cache-heavy agent loop: $1.35 vs $0.2.
Context tradeoff: GPT-5.6 Terra has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 4.6 or GPT-5.6 Terra?

GPT-5.6 Terra has the higher public score estimate, 72.58 versus 63.64, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Opus 4.6 or GPT-5.6 Terra?

GPT-5.6 Terra leads the public coding lane, 64.7 to 51.1, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Claude Opus 4.6 or GPT-5.6 Terra?

GPT-5.6 Terra leads the public agentic tasks lane, 59.5 to 44.3, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Claude Opus 4.6 or GPT-5.6 Terra?

For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.008 on GPT-5.6 Terra; repository review costs $0.325 and $0.136; the cache-heavy agent loop costs $1.35 and $0.2. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.

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

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 1M.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence56 rows

Agentic

Terminal-Bench 2.0
Claude Opus 4.665.4%
Source
GPT-5.6 Terra—
Not directly comparable
BrowseComp
Claude Opus 4.683.7%
Source
GPT-5.6 Terra87.5%
Source
GPT-5.6 Terra leads this result
OSWorld-Verified
Claude Opus 4.672.7%
Source
GPT-5.6 Terra—
Not directly comparable
Claw-Eval
Claude Opus 4.670.4%
Source
GPT-5.6 Terra—
Not directly comparable
DeepSearchQA
Claude Opus 4.673.7%
Source
GPT-5.6 Terra—
Not directly comparable
CyberGym
Claude Opus 4.666.6%
Source
GPT-5.6 Terra81.8%
Source
GPT-5.6 Terra leads this result
Gert Labs
Claude Opus 4.661.85%
Source
GPT-5.6 Terra—
Not directly comparable
ResearchClawBench
Claude Opus 4.619.9%
Source
GPT-5.6 Terra—
Not directly comparable
JobBench
Claude Opus 4.636.7%
Source
GPT-5.6 Terra—
Not directly comparable
ApprenticeBench
Shared source
Claude Opus 4.65%
GPT-5.6 Terra16%
GPT-5.6 Terra leads this result
Terminal-Bench 3.0
Claude Opus 4.6—
GPT-5.6 Terra20.8%
Source
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.6—
GPT-5.6 Terra87.4%
Source
Not directly comparable
OSWorld 2.0
Claude Opus 4.6—
GPT-5.6 Terra50.2%
Source
Not directly comparable
ExploitGym
Claude Opus 4.6—
GPT-5.6 Terra23.2%
Source
Not directly comparable
Toolathlon
Claude Opus 4.6—
GPT-5.6 Terra53.1%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6—
GPT-5.6 Terra77.5%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.680.8%
Source
GPT-5.6 Terra—
Not directly comparable
SWE-bench Verified*
Claude Opus 4.675.6%
Source
GPT-5.6 Terra—
Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%
Source
GPT-5.6 Terra—
Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%
Source
GPT-5.6 Terra63.4%
Source
GPT-5.6 Terra leads this result
SWE-Rebench
Claude Opus 4.665.3%
Source
GPT-5.6 Terra—
Not directly comparable
React Native Evals
Claude Opus 4.684.1%
Source
GPT-5.6 Terra—
Not directly comparable
Vibe Code Bench
Claude Opus 4.657.57%
Source
GPT-5.6 Terra—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.626.9%
Source
GPT-5.6 Terra—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.6—
GPT-5.6 Terra87.4%
Source
Not directly comparable
DeepSWE
Claude Opus 4.6—
GPT-5.6 Terra69.6%
Source
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 4.6—
GPT-5.6 Terra55.8%
Source
Not directly comparable
cursorBench32
Claude Opus 4.6—
GPT-5.6 Terra64.9%
Source
Not directly comparable
VulcanBench v3
Claude Opus 4.6—
GPT-5.6 Terra87.0%
Source
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.6—
GPT-5.6 Terra85.9%
Source
Not directly comparable
SWE-bench (Vals)
Claude Opus 4.6—
GPT-5.6 Terra95.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Claude Opus 4.6—
GPT-5.6 Terra83.9%
Source
Not directly comparable
ARC-AGI-3
Claude Opus 4.6—
GPT-5.6 Terra0.8%
Source
Not directly comparable

Multimodal

MMMU-Pro
Claude Opus 4.677.3%
Source
GPT-5.6 Terra80.7%
Source
GPT-5.6 Terra leads this result
ERQA
Claude Opus 4.651.6%
Source
GPT-5.6 Terra—
Not directly comparable
ScreenSpot Pro
Claude Opus 4.683.1%
Source
GPT-5.6 Terra—
Not directly comparable
MedXpertQA (MM)
Claude Opus 4.664.8%
Source
GPT-5.6 Terra—
Not directly comparable
MMMU-Pro w/ Python
Claude Opus 4.6—
GPT-5.6 Terra82%
Source
Not directly comparable

Knowledge

GPQA
Claude Opus 4.691.3%
Source
GPT-5.6 Terra92.9%
Source
GPT-5.6 Terra leads this result
GPQA-D
Claude Opus 4.689.2%
Source
GPT-5.6 Terra92.9%
Source
GPT-5.6 Terra leads this result
SuperGPQA
Claude Opus 4.695%
Source
GPT-5.6 Terra—
Not directly comparable
MMLU-Pro
Claude Opus 4.682%
Source
GPT-5.6 Terra—
Not directly comparable
MMLU-Pro (Arcee)
Claude Opus 4.689.1%
Source
GPT-5.6 Terra—
Not directly comparable
HLE
Claude Opus 4.653%
Source
GPT-5.6 Terra—
Not directly comparable
HLE w/o tools
Claude Opus 4.640%
Source
GPT-5.6 Terra—
Not directly comparable
HealthBench Hard
Claude Opus 4.614.8%
Source
GPT-5.6 Terra32.7%
Source
GPT-5.6 Terra leads this result
MedXpertQA (Text)
Claude Opus 4.652.1%
Source
GPT-5.6 Terra—
Not directly comparable
HLE-Verified
Claude Opus 4.6—
GPT-5.6 Terra51.1%
Source
Not directly comparable
LABBench2
Claude Opus 4.6—
GPT-5.6 Terra81.2%
Source
Not directly comparable
HealthBench Professional
Claude Opus 4.6—
GPT-5.6 Terra57.7%
Source
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6—
GPT-5.6 Terra90.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6—
GPT-5.6 Terra86.7%
Source
Not directly comparable

Math

AIME25 (Arcee)
Claude Opus 4.699.8%
Source
GPT-5.6 Terra—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.640.700%
Source
GPT-5.6 Terra84.900%
Source
GPT-5.6 Terra leads this result
FrontierMath v2 (Tier 4)
Claude Opus 4.622.900%
Source
GPT-5.6 Terra68.300%
Source
GPT-5.6 Terra leads this result
FrontierMath (legacy)
Claude Opus 4.6—
GPT-5.6 Terra84.9%
Source
Not directly comparable

56 public results · 10 shared

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Last updated September 24, 2026

Claude Opus 4.6 vs GPT-5.6 Terra

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Multimodal

Knowledge

Instruction following

Reasoning

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Claude Opus 4.6 or GPT-5.6 Terra?

Which is better for coding, Claude Opus 4.6 or GPT-5.6 Terra?

Which is better for agentic tasks, Claude Opus 4.6 or GPT-5.6 Terra?

Which costs less, Claude Opus 4.6 or GPT-5.6 Terra?

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Claude Opus 4.6 vs GPT-5.6 Terra