Claude Opus 4.5 vs GPT-5.6 Sol

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

Decision reading

GPT-5.6 Sol has the higher public score, 78.49 versus 55.09, and the 90% score intervals do not overlap. 7 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 4.5

Anthropic

55.09/100

Supported · Public rank #54

90% interval 42.9–67.3

Model B

GPT-5.6 Sol

OpenAI

78.49/100

Supported · Public rank #7

90% interval 75.6–81.4

Shared results: 7
Claude Opus 4.5 only: 38
GPT-5.6 Sol only: 30
Like-for-like categories: 0 / 8

Supported: Claude Opus 4.5 and GPT-5.6 SolHow 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.

Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Sol
GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
GPT-5.6 Sol
GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude Opus 4.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback

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.

43.5Claude Opus 4.571.6GPT-5.6 Sol

Directional only · BenchAlign v5.7

GPT-5.6 Sol scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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 78.8
Claude Opus 4.5:4.167%
GPT-5.6 Sol:83.000%
Claude Opus 4.5 source GPT-5.6 Sol source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 68.3
Claude Opus 4.5:20.690%
GPT-5.6 Sol:89.000%
Claude Opus 4.5 source GPT-5.6 Sol source
MMMU-ProMultimodal
Normalized gap 12.4
Claude Opus 4.5:70.6%
GPT-5.6 Sol:83%
Claude Opus 4.5 source GPT-5.6 Sol source
GPQAKnowledge
Normalized gap 7.6
Claude Opus 4.5:87%
GPT-5.6 Sol:94.6%
Claude Opus 4.5 source GPT-5.6 Sol source
SWE-bench ProCoding
Normalized gap 7.5
Claude Opus 4.5:57.1%
GPT-5.6 Sol:64.6%
Claude Opus 4.5 source GPT-5.6 Sol 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

Directional only

Claude Opus 4.5: 30.9; Estimated · #65/105
GPT-5.6 Sol: 69.6; Supported · #7/105
Basis: BenchAlign v5.7 lane · 15 vs 9 public rows
Reading: Directional only

Coding

Directional only

Claude Opus 4.5: 43.5; Estimated · #50/135
GPT-5.6 Sol: 71.6; Supported · #6/135
Basis: BenchAlign v5.7 lane · 5 vs 12 public rows
Reading: Directional only

Multimodal

Directional only

Claude Opus 4.5: 23.5; #48/50
GPT-5.6 Sol: 87.6; #5/50
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Knowledge

Directional only

Claude Opus 4.5: 49.4; Estimated · #58/158
GPT-5.6 Sol: 78.8; Supported · #7/158
Basis: BenchAlign v5.7 lane · 6 vs 8 public rows
Reading: Directional only

Instruction following

Directional only

Claude Opus 4.5: 30.6; #114/124
GPT-5.6 Sol: 87.7; #28/124
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.5: 73.2; Unranked · 4 rankable rows
GPT-5.6 Sol: 72.1; #8/19
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.5: 82.9; #2/12
GPT-5.6 Sol: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.5: 57.9; #6/7
GPT-5.6 Sol: 96.8; Unranked · 3 rankable rows
Basis: Provisional lane · 4 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.5 and GPT-5.6 Sol
Category	Claude Opus 4.5	GPT-5.6 Sol	Basis	Reading
Agentic	30.9Estimated · #65/105	69.6Supported · #7/105	Directional onlyBenchAlign v5.7 lane · 15 vs 9 public rows	Directional only
Coding	43.5Estimated · #50/135	71.6Supported · #6/135	Directional onlyBenchAlign v5.7 lane · 5 vs 12 public rows	Directional only
Multimodal	23.5#48/50	87.6#5/50	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Knowledge	49.4Estimated · #58/158	78.8Supported · #7/158	Directional onlyBenchAlign v5.7 lane · 6 vs 8 public rows	Directional only
Instruction following	30.6#114/124	87.7#28/124	Directional onlyProvisional lane · 1 vs 0 weighted rows	Directional only
Reasoning	73.2Unranked · 4 rankable rows	72.1#8/19	Not comparableProvisional lane · 1 vs 2 weighted rows	Not comparable
Multilingual	82.9#2/12	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	57.9#6/7	96.8Unranked · 3 rankable rows	Not comparableProvisional lane · 4 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 4.5: $0.0175; Fits in one request
GPT-5.6 Sol: $0.014; Fits in one request

GPT-5.6 Sol has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.5: $0.325; Fits in one request
GPT-5.6 Sol: $0.26; Fits in one request

GPT-5.6 Sol 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 4.5: $1.35; Does not fit in one request; Cached input priced at the published list-input rate
GPT-5.6 Sol: $0.36; Fits in one request

Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 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 4.5GPT-5.6 Sol

Context window

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

Claude Opus 4.5

200K

GPT-5.6 Sol

1.05M

OpenAI model catalog

API model ID

Claude Opus 4.5

Not sourced

GPT-5.6 Sol

gpt-5.6-sol

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 4.5

Not published

GPT-5.6 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

Documented inputs

Claude Opus 4.5

Not sourced

GPT-5.6 Sol

text, image

OpenAI model catalog

Documented outputs

Claude Opus 4.5

Not sourced

GPT-5.6 Sol

text

OpenAI model catalog

Provider availability

Claude Opus 4.5

Not sourced

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Claude Opus 4.5

Non-Reasoning

GPT-5.6 Sol

Reasoning

Weight access

Claude Opus 4.5

Proprietary

GPT-5.6 Sol

Proprietary

License

Claude Opus 4.5

Proprietary

GPT-5.6 Sol

Proprietary

Release date

Claude Opus 4.5

2025-11-01

GPT-5.6 Sol

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 Sol has the higher public score, 78.49 versus 55.09, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.325 vs $0.26. Cache-heavy agent loop: $1.35 vs $0.36.
Context tradeoff: GPT-5.6 Sol 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 4.5 or GPT-5.6 Sol?

GPT-5.6 Sol has the higher public score, 78.49 versus 55.09, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Opus 4.5 or GPT-5.6 Sol?

GPT-5.6 Sol scores higher for coding on the public lane, 71.6 to 43.5. Claude Opus 4.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Opus 4.5 or GPT-5.6 Sol?

GPT-5.6 Sol scores higher for agentic tasks on the public lane, 69.6 to 30.9. Claude Opus 4.5 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Opus 4.5 or GPT-5.6 Sol?

For the stated presets, chat costs $0.0175 on Claude Opus 4.5 and $0.014 on GPT-5.6 Sol; repository review costs $0.325 and $0.26; the cache-heavy agent loop costs $1.35 and $0.36. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.5 or GPT-5.6 Sol?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 200K.

Benchmark evidence

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

Browse raw public benchmark evidence75 rows

Agentic

Terminal-Bench 2.0
Claude Opus 4.559.3%
Source
GPT-5.6 Sol—
Not directly comparable
OSWorld-Verified
Claude Opus 4.566.3%
Source
GPT-5.6 Sol—
Not directly comparable
OSWorld
Claude Opus 4.566.3%
Source
GPT-5.6 Sol—
Not directly comparable
Claw-Eval
Claude Opus 4.559.6%
Source
GPT-5.6 Sol—
Not directly comparable
QwenClawBench
Claude Opus 4.552.3%
Source
GPT-5.6 Sol—
Not directly comparable
τ³-bench results
Claude Opus 4.570.2%
Source
GPT-5.6 Sol—
Not directly comparable
VITA-Bench
Claude Opus 4.523.3%
Source
GPT-5.6 Sol—
Not directly comparable
DeepPlanning
Claude Opus 4.526.4%
Source
GPT-5.6 Sol—
Not directly comparable
Toolathlon
Claude Opus 4.543.5%
Source
GPT-5.6 Sol58%
Source
GPT-5.6 Sol leads this result
MCP Atlas
Claude Opus 4.542.3%
Source
GPT-5.6 Sol—
Not directly comparable
MCP-Tasks
Claude Opus 4.571.8%
Source
GPT-5.6 Sol—
Not directly comparable
WideResearch
Claude Opus 4.576.4%
Source
GPT-5.6 Sol—
Not directly comparable
CyberGym
Claude Opus 4.550.6%
Source
GPT-5.6 Sol84.5%
Source
GPT-5.6 Sol leads this result
Gert Labs
Claude Opus 4.564.23%
Source
GPT-5.6 Sol—
Not directly comparable
JobBench
Claude Opus 4.532.3%
Source
GPT-5.6 Sol—
Not directly comparable
Terminal-Bench 3.0
Claude Opus 4.5—
GPT-5.6 Sol34.6%
Source
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.5—
GPT-5.6 Sol91.9%
Source
Not directly comparable
BrowseComp
Claude Opus 4.5—
GPT-5.6 Sol92.2%
Source
Not directly comparable
OSWorld 2.0
Claude Opus 4.5—
GPT-5.6 Sol62.6%
Source
Not directly comparable
ExploitGym
Claude Opus 4.5—
GPT-5.6 Sol33.7%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.5—
GPT-5.6 Sol85.8%
Source
Not directly comparable
ApprenticeBench
Claude Opus 4.5—
GPT-5.6 Sol26%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.580.9%
Source
GPT-5.6 Sol—
Not directly comparable
LiveCodeBench v6
Claude Opus 4.584.8%
Source
GPT-5.6 Sol—
Not directly comparable
SWE-bench Pro
Claude Opus 4.557.1%
Source
GPT-5.6 Sol64.6%
Source
GPT-5.6 Sol leads this result
SWE Multilingual
Claude Opus 4.577.5%
Source
GPT-5.6 Sol—
Not directly comparable
NL2Repo
Claude Opus 4.543.2%
Source
GPT-5.6 Sol—
Not directly comparable
Bug Hunt Bench
Claude Opus 4.5—
GPT-5.6 Sol42 fixes
Source
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.5—
GPT-5.6 Sol91.9%
Source
Not directly comparable
DeepSWE
Claude Opus 4.5—
GPT-5.6 Sol72.7%
Source
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 4.5—
GPT-5.6 Sol60.6%
Source
Not directly comparable
FrontierSWE v2
Claude Opus 4.5—
GPT-5.6 Sol32.2%
Source
Not directly comparable
cursorBench32
Claude Opus 4.5—
GPT-5.6 Sol67.2%
Source
Not directly comparable
VulcanBench v3
Claude Opus 4.5—
GPT-5.6 Sol87.0%
Source
Not directly comparable
VulcanBench CII v1
Claude Opus 4.5—
GPT-5.6 Sol86.5%
Source
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.5—
GPT-5.6 Sol82.6%
Source
Not directly comparable
SWE-bench (Vals)
Claude Opus 4.5—
GPT-5.6 Sol96.2%
Source
Not directly comparable
cursorBench40
Claude Opus 4.5—
GPT-5.6 Sol41.7%
Source
Not directly comparable

Reasoning

LongBench v2
Claude Opus 4.564.4%
Source
GPT-5.6 Sol—
Not directly comparable
AI-Needle
Claude Opus 4.574%
Source
GPT-5.6 Sol—
Not directly comparable
ARC-AGI-2
Claude Opus 4.5—
GPT-5.6 Sol92.5%
Source
Not directly comparable
ARC-AGI-3
Claude Opus 4.5—
GPT-5.6 Sol7.8%
Source
Not directly comparable
GeneBench-Pro
Claude Opus 4.5—
GPT-5.6 Sol28.7%
Source
Not directly comparable

Multimodal

MMMU-Pro
Claude Opus 4.570.6%
Source
GPT-5.6 Sol83%
Source
GPT-5.6 Sol leads this result
MathVision
Claude Opus 4.574.3%
Source
GPT-5.6 Sol—
Not directly comparable
CharXiv
Claude Opus 4.568.5%
Source
GPT-5.6 Sol—
Not directly comparable
VideoMMMU
Claude Opus 4.584.4%
Source
GPT-5.6 Sol—
Not directly comparable
ScreenSpot Pro
Claude Opus 4.545.7%
Source
GPT-5.6 Sol—
Not directly comparable
V*
Claude Opus 4.567.0%
Source
GPT-5.6 Sol—
Not directly comparable
MMMU-Pro w/ Python
Claude Opus 4.5—
GPT-5.6 Sol84.6%
Source
Not directly comparable

Knowledge

GPQA
Claude Opus 4.587%
Source
GPT-5.6 Sol94.6%
Source
GPT-5.6 Sol leads this result
SuperGPQA
Claude Opus 4.570.6%
Source
GPT-5.6 Sol—
Not directly comparable
MMLU-Pro
Claude Opus 4.589.5%
Source
GPT-5.6 Sol—
Not directly comparable
MMLU-Redux
Claude Opus 4.596.6%
Source
GPT-5.6 Sol—
Not directly comparable
C-Eval
Claude Opus 4.592.2%
Source
GPT-5.6 Sol—
Not directly comparable
HLE
Claude Opus 4.530.8%
Source
GPT-5.6 Sol—
Not directly comparable
GPQA-D
Claude Opus 4.5—
GPT-5.6 Sol94.6%
Source
Not directly comparable
HLE-Verified
Claude Opus 4.5—
GPT-5.6 Sol54.5%
Source
Not directly comparable
LABBench2
Claude Opus 4.5—
GPT-5.6 Sol82.1%
Source
Not directly comparable
HealthBench Professional
Claude Opus 4.5—
GPT-5.6 Sol60.5%
Source
Not directly comparable
HealthBench Hard
Claude Opus 4.5—
GPT-5.6 Sol33.1%
Source
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.5—
GPT-5.6 Sol95.2%
Source
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.5—
GPT-5.6 Sol89.1%
Source
Not directly comparable

Multilingual

MMLU-ProX
Claude Opus 4.585.7%
Source
GPT-5.6 Sol—
Not directly comparable
NOVA-63
Claude Opus 4.556.7%
Source
GPT-5.6 Sol—
Not directly comparable

Instruction following

IFEval
Claude Opus 4.590.9%
Source
GPT-5.6 Sol—
Not directly comparable
IFBench
Claude Opus 4.558%
Source
GPT-5.6 Sol—
Not directly comparable

Math

AIME26
Claude Opus 4.595.1%
Source
GPT-5.6 Sol—
Not directly comparable
HMMT Feb 2025
Claude Opus 4.592.9%
Source
GPT-5.6 Sol—
Not directly comparable
HMMT Nov 2025
Claude Opus 4.593.3%
Source
GPT-5.6 Sol—
Not directly comparable
HMMT Feb 2026
Claude Opus 4.585.3%
Source
GPT-5.6 Sol—
Not directly comparable
MMAnswerBench
Claude Opus 4.584.0%
Source
GPT-5.6 Sol—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.520.690%
Source
GPT-5.6 Sol89.000%
Source
GPT-5.6 Sol leads this result
FrontierMath v2 (Tier 4)
Claude Opus 4.54.167%
Source
GPT-5.6 Sol83.000%
Source
GPT-5.6 Sol leads this result
FrontierMath (legacy)
Claude Opus 4.5—
GPT-5.6 Sol89%
Source
Not directly comparable

75 public results · 7 shared

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Last updated September 24, 2026

Claude Opus 4.5 vs GPT-5.6 Sol

Decision reading

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop 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 4.5 or GPT-5.6 Sol?

Which is better for coding, Claude Opus 4.5 or GPT-5.6 Sol?

Which is better for agentic tasks, Claude Opus 4.5 or GPT-5.6 Sol?

Which costs less, Claude Opus 4.5 or GPT-5.6 Sol?

Which has the larger context window, Claude Opus 4.5 or GPT-5.6 Sol?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Claude Opus 4.5 vs GPT-5.6 Sol