Claude Opus 5 vs GPT-6 Sol

Decision reading

Claude Opus 5 has the higher public score estimate, 80.93 versus 80.45, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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 5

Anthropic

80.93/100

Supported · Public rank #6

90% interval 77.6–84.3

Model B

GPT-6 Sol

OpenAI

80.45/100

Estimated · Public rank #7

90% interval 51.1–92.0

Updated September 22, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.

Share or export

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-6 Sol
GPT-6 Sol has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-6 Sol
GPT-6 Sol 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-6 Sol
GPT-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-6 Sol
GPT-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
GPT-6 Sol 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
GPT-6 Sol is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited

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.

76.0Claude Opus 574.3GPT-6 Sol

Directional only · BenchAlign

Claude Opus 5 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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

67 Claude Opus 5 only

7 Shared

3 GPT-6 Sol only

Shared results: 7
Claude Opus 5 only: 67
GPT-6 Sol only: 3
Like-for-like categories: 0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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 5: 78.8; Supported · #3/157
GPT-6 Sol: 69.7; Estimated · #7/157
Basis: BenchAlign lane · 20 vs 4 public rows
Reading: Directional only

Coding

Directional only

Claude Opus 5: 76.0; Supported · #5/159
GPT-6 Sol: 74.3; Estimated · #6/159
Basis: BenchAlign lane · 16 vs 1 public rows
Reading: Directional only

Reasoning

Directional only

Claude Opus 5: 75.7; #9/17
GPT-6 Sol: 78.5; #5/17
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Directional only

Knowledge

Directional only

Claude Opus 5: 81.7; Supported · #5/189
GPT-6 Sol: 80.2; Estimated · #6/189
Basis: BenchAlign lane · 19 vs 5 public rows
Reading: Directional only

Multimodal

Not comparable

Claude Opus 5: 88.8; #2/50
GPT-6 Sol: 82.8; Unranked · 1 rankable row
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 5: Not ranked
GPT-6 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 5: Not ranked
GPT-6 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 5: Not ranked
GPT-6 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 5 and GPT-6 Sol
Category	Claude Opus 5	GPT-6 Sol	Basis	Reading
Agentic	78.8Supported · #3/157	69.7Estimated · #7/157	Directional onlyBenchAlign lane · 20 vs 4 public rows	Directional only
Coding	76.0Supported · #5/159	74.3Estimated · #6/159	Directional onlyBenchAlign lane · 16 vs 1 public rows	Directional only
Reasoning	75.7#9/17	78.5#5/17	Directional onlyProvisional lane · 2 vs 0 weighted rows	Directional only
Knowledge	81.7Supported · #5/189	80.2Estimated · #6/189	Directional onlyBenchAlign lane · 19 vs 5 public rows	Directional only
Multimodal	88.8#2/50	82.8Unranked · 1 rankable row	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

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

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.

OSWorld 2.0
Agentic
Claude Opus 5: 70.6%GPT-6 Sol: 60.5%Normalized gap 10.1Claude Opus 5 source GPT-6 Sol source
AutomationBench
Agentic
Claude Opus 5: 26.0%GPT-6 Sol: 33.2%Normalized gap 7.2Claude Opus 5 source GPT-6 Sol source

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 5: $0.0175; Fits in one request
GPT-6 Sol: $0.007; Fits in one request

GPT-6 Sol has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5: $0.325; Fits in one request
GPT-6 Sol: $0.13; Fits in one request

GPT-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 5: $0.45; Fits in one request
GPT-6 Sol: $0.18; Fits in one request

GPT-6 Sol has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

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

SpecificationClaude Opus 5GPT-6 Sol

Context window

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

Claude Opus 5

Anthropic model overview

GPT-6 Sol

1.05M

OpenAI GPT-6 Sol model documentation

API model ID

Claude Opus 5

claude-opus-5

Anthropic model overview

GPT-6 Sol

gpt-6-sol

OpenAI GPT-6 Sol model documentation

Cached-input rate

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

Claude Opus 5

$0.5 per 1M cached input tokens

Claude API pricing

GPT-6 Sol

$0.2 per 1M cached input tokens

OpenAI GPT-6 Sol model documentation

Documented inputs

Claude Opus 5

text, image

Anthropic model overview

GPT-6 Sol

text, image

OpenAI GPT-6 Sol model documentation

Documented outputs

Claude Opus 5

text

Anthropic model overview

GPT-6 Sol

text

OpenAI GPT-6 Sol model documentation

Provider availability

Claude Opus 5

Generally Available · Claude API

Anthropic model overview

GPT-6 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex

OpenAI GPT-6 Sol and Luna launch

Reasoning profile

Claude Opus 5

Reasoning

GPT-6 Sol

Reasoning

Weight access

Claude Opus 5

Proprietary

GPT-6 Sol

Proprietary

License

Claude Opus 5

Proprietary

GPT-6 Sol

Proprietary

Release date

Claude Opus 5

2026-07-24

GPT-6 Sol

2026-09-16

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: Claude Opus 5 has the higher public score estimate, 80.93 versus 80.45, but the 90% score intervals overlap.
Workload cost: Repository review: $0.325 vs $0.13. Cache-heavy agent loop: $0.45 vs $0.18.
Context tradeoff: GPT-6 Sol has the larger documented window (1.05M).

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

Benchmark evidence

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

Browse raw public benchmark evidence77 rows

Agentic

Terminal-Bench 3.0
Claude Opus 542.7%
Source
GPT-6 Sol—
Not directly comparable
BrowseComp
Claude Opus 590.8%
Source
GPT-6 Sol—
Not directly comparable
HLE w/ tools
Claude Opus 564.7%
Source
GPT-6 Sol—
Not directly comparable
DeepSearchQA
Claude Opus 595.0%
Source
GPT-6 Sol—
Not directly comparable
DRACO
Claude Opus 588.6%
Source
GPT-6 Sol—
Not directly comparable
BrowseComp (10-agent, prerelease)
Claude Opus 593.6%
Source
GPT-6 Sol—
Not directly comparable
OSWorld 2.0
Claude Opus 570.6%
Source
GPT-6 Sol60.5%
Source
Claude Opus 5 leads this result
MCP Atlas
Claude Opus 585.8%
Source
GPT-6 Sol—
Not directly comparable
MCP-Atlas claim coverage
Claude Opus 589.1%
Source
GPT-6 Sol—
Not directly comparable
LAB all-pass (Anthropic harness)
Claude Opus 523.58%
Source
GPT-6 Sol—
Not directly comparable
LAB criterion-pass (Anthropic harness)
Claude Opus 593.74%
Source
GPT-6 Sol—
Not directly comparable
LAB all-pass (Harvey held-out)
Claude Opus 511.7%
Source
GPT-6 Sol—
Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 594.1%
Source
GPT-6 Sol—
Not directly comparable
Toolathlon-Verified
Claude Opus 580.6%
Source
GPT-6 Sol—
Not directly comparable
Toolathlon Verified Pass@3
Claude Opus 587.0%
Source
GPT-6 Sol—
Not directly comparable
Toolathlon Verified Pass³
Claude Opus 573.1%
Source
GPT-6 Sol—
Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 523.5 turns
Source
GPT-6 Sol—
Not directly comparable
AutomationBench
Claude Opus 526.0%
Source
GPT-6 Sol33.2%
Source
GPT-6 Sol leads this result
Terminal-Bench 2.1 (Vals)
Claude Opus 584.6%
Source
GPT-6 Sol—
Not directly comparable
ApprenticeBench
Claude Opus 536%
Source
GPT-6 Sol—
Not directly comparable
Agents' Last Exam
Claude Opus 5—
GPT-6 Sol56.4%
Source
Not directly comparable
ExploitGym
Claude Opus 5—
GPT-6 Sol22.1%
Source
Not directly comparable

Coding

Bug Hunt Bench
Claude Opus 527 fixes
Source
GPT-6 Sol—
Not directly comparable
SWE-bench Verified
Claude Opus 596%
Source
GPT-6 Sol—
Not directly comparable
SWE-bench Pro
Claude Opus 579.2%
Source
GPT-6 Sol—
Not directly comparable
SWE Multilingual
Claude Opus 589.5%
Source
GPT-6 Sol—
Not directly comparable
SWE Multimodal
Claude Opus 559.4%
Source
GPT-6 Sol—
Not directly comparable
DeepSWE
Claude Opus 568.8%
Source
GPT-6 Sol68.8%
Source
Tie
FrontierCode 1.1 Main
Claude Opus 553.4%
Source
GPT-6 Sol—
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 563.6%
Source
GPT-6 Sol—
Not directly comparable
FrontierSWE v2
Claude Opus 552.0%
Source
GPT-6 Sol—
Not directly comparable
ProgramBench (episode 1)
Claude Opus 583.0%
Source
GPT-6 Sol—
Not directly comparable
ProgramBench
Claude Opus 593.0%
Source
GPT-6 Sol—
Not directly comparable
cursorBench32
Claude Opus 570.0%
Source
GPT-6 Sol—
Not directly comparable
VulcanBench v3
Claude Opus 587.0%
Source
GPT-6 Sol—
Not directly comparable
VulcanBench CII v1
Claude Opus 596.4%
Source
GPT-6 Sol—
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 589.0%
Source
GPT-6 Sol—
Not directly comparable
SWE-bench (Vals)
Claude Opus 597.0%
Source
GPT-6 Sol—
Not directly comparable

Reasoning

ARC-AGI-1
Claude Opus 597.50%
Source
GPT-6 Sol—
Not directly comparable
ARC-AGI-2
Claude Opus 590.4%
Source
GPT-6 Sol—
Not directly comparable
ARC-AGI-3
Claude Opus 530.2%
Source
GPT-6 Sol—
Not directly comparable

Multimodal

Chartography (no tools)
Claude Opus 529.6%
Source
GPT-6 Sol—
Not directly comparable
Chartography (tools)
Claude Opus 583.0%
Source
GPT-6 Sol—
Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Opus 50.366
Source
GPT-6 Sol—
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 50.821
Source
GPT-6 Sol—
Not directly comparable
GDP.pdf (no tools)
Claude Opus 583.4%
Source
GPT-6 Sol—
Not directly comparable
GDP.pdf (tools)
Claude Opus 585.5%
Source
GPT-6 Sol—
Not directly comparable
OfficeQA
Claude Opus 578.1%
Source
GPT-6 Sol—
Not directly comparable
OfficeQA Pro
Claude Opus 566.9%
Source
GPT-6 Sol—
Not directly comparable

Knowledge

HLE
Claude Opus 564.7%
Source
GPT-6 Sol—
Not directly comparable
HLE w/o tools
Claude Opus 556.3%
Source
GPT-6 Sol—
Not directly comparable
HLE-Verified
Claude Opus 554.4%
Source
GPT-6 Sol—
Not directly comparable
LABBench2
Claude Opus 584.2%
Source
GPT-6 Sol—
Not directly comparable
HealthBench (raw)
Claude Opus 567.1%
Source
GPT-6 Sol47.1%
Source
Claude Opus 5 leads this result
HealthBench (length-adjusted)
Claude Opus 557.8%
Source
GPT-6 Sol53.2%
Source
Claude Opus 5 leads this result
HealthBench Professional
Claude Opus 559.8%
Source
GPT-6 Sol60.8%
Source
GPT-6 Sol leads this result
HealthBench Professional (raw)
Claude Opus 573.4%
Source
GPT-6 Sol59.5%
Source
Claude Opus 5 leads this result
BioMysteryBench (human-solvable)
Claude Opus 590.1%
Source
GPT-6 Sol—
Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 549.4%
Source
GPT-6 Sol—
Not directly comparable
SpatialBench Verified
Claude Opus 572.5%
Source
GPT-6 Sol—
Not directly comparable
SingleCellBench
Claude Opus 560.6%
Source
GPT-6 Sol—
Not directly comparable
ProteinGym Hard
Claude Opus 547.7%
Source
GPT-6 Sol—
Not directly comparable
Protein Design
Claude Opus 542.5%
Source
GPT-6 Sol—
Not directly comparable
Organic chemistry V2
Claude Opus 561.6%
Source
GPT-6 Sol—
Not directly comparable
Protocols (troubleshooting)
Claude Opus 561.1%
Source
GPT-6 Sol—
Not directly comparable
Protocols (understanding)
Claude Opus 578.4%
Source
GPT-6 Sol—
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 593.4%
Source
GPT-6 Sol—
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 591.6%
Source
GPT-6 Sol—
Not directly comparable
HealthBench Hard
Claude Opus 5—
GPT-6 Sol30.1%
Source
Not directly comparable

Multilingual

GMMLU
Claude Opus 592.5%
Source
GPT-6 Sol—
Not directly comparable
MILU
Claude Opus 592.1%
Source
GPT-6 Sol—
Not directly comparable
INCLUDE
Claude Opus 589.8%
Source
GPT-6 Sol—
Not directly comparable

Math

IMO 2026
Claude Opus 542/42
Source
GPT-6 Sol—
Not directly comparable
RiemannBench (no tools)
Claude Opus 560.0%
Source
GPT-6 Sol—
Not directly comparable
RiemannBench (tools)
Claude Opus 579.0%
Source
GPT-6 Sol—
Not directly comparable
ArXivMath Jun. 2026 (no tools)
Claude Opus 590.8%
Source
GPT-6 Sol—
Not directly comparable
ArXivMath Jun. 2026 (tools)
Claude Opus 591.3%
Source
GPT-6 Sol—
Not directly comparable

Questions

Which is better, Claude Opus 5 or GPT-6 Sol?

Claude Opus 5 has the higher public score estimate, 80.93 versus 80.45, 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 5 or GPT-6 Sol?

Claude Opus 5 scores higher for coding on the public lane, 76 to 74.3. GPT-6 Sol 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 5 or GPT-6 Sol?

Claude Opus 5 scores higher for agentic tasks on the public lane, 78.8 to 69.7. GPT-6 Sol 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 5 or GPT-6 Sol?

For the stated presets, chat costs $0.0175 on Claude Opus 5 and $0.007 on GPT-6 Sol; repository review costs $0.325 and $0.13; the cache-heavy agent loop costs $0.45 and $0.18. Costs use the listed standard API rates.

Which has the larger context window, Claude Opus 5 or GPT-6 Sol?

GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 22, 2026

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Claude Opus 5 vs GPT-6 Sol

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

Which one for a specific job

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Multimodal

Multilingual

Instruction following

Math

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Math

Questions

Which is better, Claude Opus 5 or GPT-6 Sol?

Which is better for coding, Claude Opus 5 or GPT-6 Sol?

Which is better for agentic tasks, Claude Opus 5 or GPT-6 Sol?

Which costs less, Claude Opus 5 or GPT-6 Sol?

Which has the larger context window, Claude Opus 5 or GPT-6 Sol?

Related comparisons

Watch Claude Opus 5 vs GPT-6 Sol