Head-to-head comparison

Claude Opus 4.8 vs. GPT-5.6 Luna

Compare published benchmark results from matching versions and cohorts, API pricing, and technical specifications. Any documented test setup differences remain visible.

shared result series: 27
distinct benchmarks: 27
latest retrieval: 2026-09-29

First modelSecond model

Specifications

General specifications

Release date, availability, context window, architecture, and published parameter counts at a glance. Undisclosed details remain marked as unavailable.

Specifications for Claude Opus 4.8 and GPT-5.6 Luna
Attribute	Claude Opus 4.8	GPT-5.6 Luna
Provider	Anthropic	OpenAI
Released	May 2026	Jul 9, 2026
Availability	Legacy	Active
Model type	Proprietary	Proprietary
Parameters	Not published	Not published
Architecture	Not published	Not published
Context window	1,000,000 tokens	1,050,000 tokens
Knowledge cutoff	January 2026	February 16, 2026
Technical sources	Model, Knowledge cutoff	Model, Context, Knowledge cutoff

Pricing

API pricing compared

The table compares official standard API rates per 1M tokens, including published cache pricing. Batch, fast, priority, regional surcharges, tool calls, and cloud platform pricing are excluded.

API pricing for Claude Opus 4.8 and GPT-5.6 Luna
Price type	Claude Opus 4.8	GPT-5.6 Luna
API input	$5	$0.2 ≤272K / $0.4 >272K
API output	$25	$1.2 ≤272K / $1.8 >272K
Cache read	$0.5	$0.02 ≤272K / $0.04 >272K
Cache write (5 min.)	$6.25	$0.25 ≤272K / $0.5 >272K
Cache write (1 hr.)	$10	Not listed
Price verified	09/22/2026 Source	09/22/2026 Source

Claude Opus 4.8GPT-5.6 Luna

API inputUSD per 1M tokens, base tier

Claude Opus 4.8$5

GPT-5.6 Luna$0.2

API outputUSD per 1M tokens, base tier

Claude Opus 4.8$25

GPT-5.6 Luna$1.2

Cache readUSD per 1M tokens, base tier

Claude Opus 4.8$0.5

GPT-5.6 Luna$0.02

Key to the marks:better valuebetter, but not conclusivetie

Pricing by provider

Direct developer pricing and provider endpoint tariffs routed through OpenRouter are listed separately. Prices are in USD per 1M tokens. Context-dependent direct tiers plus regional, Flex, Priority, and other OpenRouter tariffs remain individually identifiable.

No provider tariff for Claude Opus 4.8 is available in the stored OpenRouter snapshot. The official direct price is still shown.

Provider pricing for Claude Opus 4.8 and GPT-5.6 Luna
Model	Provider	Purchase route and tariff	Input per 1M tokens	Output per 1M tokens	Source
Claude Opus 4.8	Anthropic	Direct from developerStandard API	Lowest listed price: $5	Lowest listed price: $25	Source09/22/2026
GPT-5.6 Luna	OpenAI	Direct from developerUp to 272,000 context tokens	$0.2	$1.2	Source09/22/2026
	OpenAI	Direct from developerAbove 272,000 context tokens	$0.4	$1.8	Source09/22/2026
	Amazon Bedrock	Via OpenRouteramazon-bedrock/us-east-1	$0.22	$1.32	Source10/07/2026
	Azure	Via OpenRouterazure	$0.2	$1.2	Source10/07/2026
	Azure	Via OpenRouterazure/eu	$0.22	$1.32	Source10/07/2026
	Azure	Via OpenRouterazure/us	$0.22	$1.32	Source10/07/2026
	OpenAI	Via OpenRouteropenai	$0.2	$1.2	Source10/07/2026
	OpenAI	Via OpenRouteropenai/fast	$0.4	$2.4	Source10/07/2026
	OpenAI	Via OpenRouteropenai/flex	Lowest listed price: $0.1	Lowest listed price: $0.6	Source10/07/2026

Performance

Shared benchmarks

Only published results with the same benchmark version, task, metric, and comparison cohort are paired. Different reasoning levels, agents, harnesses, or output limits appear directly in the table.

Capability profile from matched benchmarks

Each axis averages directly matched benchmark families. A value of 100 means the stronger value within this pair, not a universal quality score.

Claude Opus 4.8

GPT-5.6 Luna

Coding: 99.1 / 63.1 (5 benchmark families)

Reasoning: 100 / 99.2 (1 benchmark family)

Knowledge: 100 / 96 (1 benchmark family)

Finance: 99.5 / 97.2 (6 benchmark families)

Legal: 99.8 / 65.5 (3 benchmark families)

Agentic tasks: 94.5 / 100 (2 benchmark families)

Multimodal: 100 / 98.2 (1 benchmark family)

Search: 100 / 98.8 (1 benchmark family)

27 matched rows from 26 benchmark families were considered. The radar shows 8 of 13 comparable categories.

Claude Opus 4.8GPT-5.6 Luna

BrowseCompOverall. Higher is better. No documented setup difference

Claude Opus 4.884.3 %

GPT-5.6 Luna83.3 %

Code MigrationOverall. Higher is better. No documented setup difference

Claude Opus 4.847.25 %

GPT-5.6 Luna44.55 %

CorpFin v2Overall. Higher is better. No documented setup difference

Claude Opus 4.866.706 %

GPT-5.6 Luna64.219 %

EMBOverall. Higher is better. No documented setup difference

Claude Opus 4.869.373 %

GPT-5.6 Luna67.116 %

Finance Agent (v2)Overall. Higher is better. No documented setup difference

Claude Opus 4.853.918 %

GPT-5.6 Luna55.044 %

GPQA DiamondOverall. Higher is better. No documented setup difference

Claude Opus 4.892.424 %

GPT-5.6 Luna91.666 %

Harvey's Legal Agent BenchmarkOverall · Task fully resolved. Higher is better. No documented setup difference

Claude Opus 4.89.583 %

GPT-5.6 Luna1.25 %

Legal Research BenchOverall · All-pass. Higher is better. No documented setup difference

Claude Opus 4.843.75 %

GPT-5.6 Luna36.538 %

LegalBenchOverall. Higher is better. No documented setup difference

Claude Opus 4.883.568 %

GPT-5.6 Luna84.034 %

MedCodeOverall. Higher is better. No documented setup difference

Claude Opus 4.853.217 %

GPT-5.6 Luna42.391 %

MedScribeOverall. Higher is better. No documented setup difference

Claude Opus 4.885.755 %

GPT-5.6 Luna84.391 %

MMLU-ProOverall. Higher is better. No documented setup difference

Claude Opus 4.889.585 %

GPT-5.6 Luna86.036 %

Key to the marks:better valuebetter, but not conclusivetie

Benchmark scores for Claude Opus 4.8 and GPT-5.6 Luna
Benchmark and task	Claude Opus 4.8	GPT-5.6 Luna	Source
BrowseCompOverall Test details No documented difference in the test setupVersion: 1,266 tasksMetric: accuracyScoring: Higher is betterCross-vendor comparison table with browsing tools. Exact agent systems differ.	Winner84.3 % Editorial selection from the vendor table, not a complete extract of the comparison cohort.	83.3 % Editorial selection from the vendor table, not a complete extract of the comparison cohort.	OpenAI Source details Data as of: 07/09/2026
Code MigrationOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner47.25 %	44.55 %	Vals AI Source details Data as of: 09/27/2026
CorpFin v2Overall Test details No documented difference in the test setupVersion: 2Metric: accuracyScoring: Higher is better	Winner66.706 %	64.219 %	Vals AI Source details Data as of: 08/12/2026
EMBOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner69.373 %	67.116 %	Vals AI Source details Data as of: 09/27/2026
Finance Agent (v2)Overall Test details No documented difference in the test setupVersion: 2Metric: accuracyScoring: Higher is better	53.918 %	Winner55.044 %	Vals AI Source details Data as of: 09/27/2026
GPQA DiamondOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner92.424 %	91.666 %	Vals AI Source details Data as of: 09/01/2026
Harvey's Legal Agent BenchmarkOverall · Task fully resolved Test details No documented difference in the test setupVersion: 1Metric: task resolution rateScoring: Higher is better	Winner9.583 %	1.25 %	Vals AI Source details Data as of: 09/27/2026
Legal Research BenchOverall · All-pass Test details No documented difference in the test setupVersion: 1Metric: all-pass rateScoring: Higher is better	Winner43.75 %	36.538 %	Vals AI Source details Data as of: 09/27/2026
LegalBenchOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	83.568 %	Winner84.034 %	Vals AI Source details Data as of: 09/27/2026
MedCodeOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner53.217 %	42.391 %	Vals AI Source details Data as of: 09/26/2026
MedScribeOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner85.755 %	84.391 %	Vals AI Source details Data as of: 09/26/2026
MMLU-ProOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner89.585 %	86.036 %	Vals AI Source details Data as of: 09/01/2026
MMMU-ProOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner86.59 %	85.029 %	Vals AI Source details Data as of: 09/01/2026
MortgageTaxOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner69.912 %	67.29 %	Vals AI Source details Data as of: 09/01/2026
ProgramBenchOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner1 %	0 %	Vals AI Source details Data as of: 09/27/2026
Public Benefits Bench v1.1Overall Test details No documented difference in the test setupVersion: 1.1Metric: accuracyScoring: Higher is better	Winner68.133 %	61.164 %	Vals AI Source details Data as of: 09/27/2026
SAGEOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner54.788 %	44.22 %	Vals AI Source details Data as of: 09/26/2026
SkillsBenchOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is betterSettings: Harness: OpenHands	59.227 %	Winner60.447 %	Vals AI Source details Data as of: 09/27/2026
SWE-bench VerifiedOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	88.6 %	Winner93 %	Vals AI Source details Data as of: 09/01/2026
Tax Agent BenchOverall Test details No documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better	Winner64.906 %	60.815 %	Vals AI Source details Data as of: 09/27/2026
TaxEval v2Overall Test details No documented difference in the test setupVersion: 2Metric: accuracyScoring: Higher is better	75.634 %	Winner76.166 %	Vals AI Source details Data as of: 09/01/2026
Terminal-Bench 2.1Overall Test details No documented difference in the test setupVersion: 2.1Metric: accuracyScoring: Higher is better	71.91 %	Winner79.026 %	Vals AI Source details Data as of: 09/27/2026
Terminal-Bench 4.0Overall Test details No documented difference in the test setupVersion: 4.0Metric: accuracyScoring: Higher is better	Winner16.162 %	4.545 %	Vals AI Source details Data as of: 09/27/2026
Terminal-Bench ScienceOverall Test details No documented difference in the test setupVersion: 0.1Metric: accuracyScoring: Higher is better	Winner10 %	5.714 %	Vals AI Source details Data as of: 09/27/2026
Vals IndexOverall Test details No documented difference in the test setupVersion: 2Metric: weighted index scoreScoring: Higher is better	Winner60.908 %	59.881 %	Vals AI Source details Data as of: 09/27/2026
Vals Multimodal IndexOverall Test details No documented difference in the test setupVersion: 1.2Metric: weighted index scoreScoring: Higher is better	Winner70.889 %	69.058 %	Vals AI Source details Data as of: 08/11/2026
Vibe Code Bench v1.1Overall Test details No documented difference in the test setupVersion: 1.1Metric: accuracyScoring: Higher is betterSettings: Harness: OpenHands	Winner82.725 %	77.055 %	Vals AI Source details Data as of: 09/27/2026
Key to the marks:better valuebetter, but not conclusivetie

Our model tests

Our tests

Compare how the models respond to the same prompt. Each test shows the latest attempt, with no subsequent fixes to the generated code. Earlier runs remain available in the test details. These results do not contribute to an overall score.

Pelican on a bicycle

A community classic for free-form SVG drawing.

Claude Opus 4.8

Reasoning (requested): high

Current attempt

Run date: 1 Oct 2026
API provider: openrouter
Requested API model: anthropic/claude-opus-4.8
API model reported in response: anthropic/claude-opus-4.8
Reasoning setting: high
Pinned endpoint: Anthropic | anthropic/claude-4.8-opus-20260528
Temperature: Provider default
Token limit including reasoning: 8,192 tokens
Test protocol: community-visual-tests-v1
Technical checks: Passed, not a quality rating
Response time: 18 s

Visually reviewed. This individual test does not establish overall model quality.

GPT-5.6 Luna

Reasoning (requested): high

Current attempt

Run date: 7 Sept 2026
API provider: openrouter
Requested API model: openai/gpt-5.6-luna
API model reported in response: openai/gpt-5.6-luna
Reasoning setting: high
Pinned endpoint: OpenAI | openai/gpt-5.6-luna-20260709
Temperature: Provider default
Token limit including reasoning: 8,192 tokens
Test protocol: community-visual-tests-v1
Technical checks: Passed, not a quality rating
Response time: 49.9 s

Visually reviewed. This individual test does not establish overall model quality.

Prompt and test conditions

Generate an SVG of a pelican riding a bicycle

Token limit including reasoning: 8,192 tokens

Task origin (Simon Willison)

How to read this comparison

Winning one benchmark is not an overall verdict

Your workload, budget, and required context length matter most. A coding benchmark says little about visual understanding or agent performance.

The standard price table uses direct developer rates. The provider comparison labels OpenRouter endpoints, regions, and special tariffs separately.

Every result links to its measurement source and retrieval date. Labels distinguish vendor reports, official benchmark leaderboards, and independent evaluations. Disputed or archived results are excluded.

More matchups

Compare Claude Opus 4.8 and GPT-5.6 Luna with other leading models using the same data and benchmark logic.

View the complete LLM comparison