Long documents
Prompts that approach the documented context limit
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the larger documented context window.
Confidence: documented
Claude Opus 4.7 (Adaptive) vs GPT-5.3 Codex
Updated September 29, 2026. Rank says Claude Opus 4.7 (Adaptive) is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Claude Opus 4.7 (Adaptive) has the higher public score estimate, 68.46 versus 61.81, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model A
Anthropic
68.46/100
Estimated · Public rank #20
90% interval 57.0–80.0
- Shared results
- 5
- Claude Opus 4.7 (Adaptive) only
- 16
- GPT-5.3 Codex only
- 5
- Like-for-like categories
- 0 / 8
Estimated: Claude Opus 4.7 (Adaptive) · Supported: GPT-5.3 CodexHow 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.3 Codex 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
57.9Claude Opus 4.7 (Adaptive)55.5GPT-5.3 Codex
Directional only · BenchAlign v5.7
Claude Opus 4.7 (Adaptive) 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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
16 Claude Opus 4.7 (Adaptive) only
5 Shared
5 GPT-5.3 Codex only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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-VerifiedAgentic
Normalized gap 13.3Claude Opus 4.7 (Adaptive):78%GPT-5.3 Codex:64.7%Terminal-Bench 2.0Agentic
Normalized gap 7.9Claude Opus 4.7 (Adaptive):69.4%GPT-5.3 Codex:77.3%SWE-bench ProCoding
Normalized gap 7.5Claude Opus 4.7 (Adaptive):64.3%GPT-5.3 Codex:56.8%SWE-bench VerifiedCoding
Normalized gap 2.6Claude Opus 4.7 (Adaptive):87.6%GPT-5.3 Codex:85%
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 4.7 (Adaptive)
- 59.1
- Supported · #22/117
- GPT-5.3 Codex
- 54.1
- Estimated · #34/117
- Basis
- BenchAlign v5.7 lane · 7 vs 4 public rows
- Reading
- Directional only
Coding
Directional only- Claude Opus 4.7 (Adaptive)
- 57.9
- Estimated · #24/143
- GPT-5.3 Codex
- 55.5
- Supported · #29/143
- Basis
- BenchAlign v5.7 lane · 3 vs 6 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Opus 4.7 (Adaptive)
- 63.9
- Estimated · #29/169
- GPT-5.3 Codex
- 63.8
- Estimated · #31/169
- Basis
- BenchAlign v5.7 lane · 4 vs 0 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.7 (Adaptive)
- 53.6
- Unranked · 3 rankable rows
- GPT-5.3 Codex
- 79.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7 (Adaptive)
- 50.1
- #39/50
- GPT-5.3 Codex
- 76.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- GPT-5.3 Codex
- 91.2
- #14/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 (Adaptive) | GPT-5.3 Codex | Basis | Reading |
|---|---|---|---|---|
| Agentic | 59.1Supported · #22/117 | 54.1Estimated · #34/117 | Directional onlyBenchAlign v5.7 lane · 7 vs 4 public rows | Directional only |
| Coding | 57.9Estimated · #24/143 | 55.5Supported · #29/143 | Directional onlyBenchAlign v5.7 lane · 3 vs 6 public rows | Directional only |
| Knowledge | 63.9Estimated · #29/169 | 63.8Estimated · #31/169 | Directional onlyBenchAlign v5.7 lane · 4 vs 0 public rows | Directional only |
| Reasoning | 53.6Unranked · 3 rankable rows | 79.6Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 50.1#39/50 | 76.7Unranked · 1 rankable row | Not comparableProvisional lane · 2 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 | 91.2#14/124 | 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 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 4.7 (Adaptive)
- $0.0175
- Fits in one request
- GPT-5.3 Codex
- $0.00875
- Fits in one request
GPT-5.3 Codex has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7 (Adaptive)
- $0.325
- Fits in one request
- GPT-5.3 Codex
- $0.1295
- Fits in one request
GPT-5.3 Codex 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 4.7 (Adaptive)
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.3 Codex has the lower modeled cost
Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex 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 4.7 (Adaptive)GPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7 (Adaptive)
1M
GPT-5.3 Codex
API model ID
Claude Opus 4.7 (Adaptive)
Not sourced
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.7 (Adaptive)
Not published
GPT-5.3 Codex
Not published
Documented inputs
Claude Opus 4.7 (Adaptive)
Not sourced
GPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
Claude Opus 4.7 (Adaptive)
Not sourced
GPT-5.3 Codex
Provider availability
Claude Opus 4.7 (Adaptive)
Not sourced
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Opus 4.7 (Adaptive)
Reasoning
GPT-5.3 Codex
Reasoning
Weight access
Claude Opus 4.7 (Adaptive)
Proprietary
GPT-5.3 Codex
Proprietary
License
Claude Opus 4.7 (Adaptive)
Proprietary
GPT-5.3 Codex
Proprietary
Release date
Claude Opus 4.7 (Adaptive)
2026-04-16
GPT-5.3 Codex
2026-02-05
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 4.7 (Adaptive) has the higher public score estimate, 68.46 versus 61.81, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.1295. Cache-heavy agent loop: $1.35 vs $0.525.
- Context tradeoff
- Claude Opus 4.7 (Adaptive) has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 4.7 (Adaptive) or GPT-5.3 Codex?
Claude Opus 4.7 (Adaptive) has the higher public score estimate, 68.46 versus 61.81, 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 4.7 (Adaptive) or GPT-5.3 Codex?
Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 57.9 to 55.5. Claude Opus 4.7 (Adaptive) 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 4.7 (Adaptive) or GPT-5.3 Codex?
Claude Opus 4.7 (Adaptive) scores higher for agentic tasks on the public lane, 59.1 to 54.1. GPT-5.3 Codex 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 4.7 (Adaptive) or GPT-5.3 Codex?
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.00875 on GPT-5.3 Codex; repository review costs $0.325 and $0.1295; the cache-heavy agent loop costs $1.35 and $0.525. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.7 (Adaptive) or GPT-5.3 Codex?
Claude Opus 4.7 (Adaptive) has the larger documented context window: 1M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence26 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%SourceGPT-5.3 Codex77.3%SourceGPT-5.3 Codex leads this result
BrowseComp
Claude Opus 4.7 (Adaptive)79.3%SourceGPT-5.3 Codex—Not directly comparable
MCP Atlas
Claude Opus 4.7 (Adaptive)77.3%SourceGPT-5.3 Codex—Not directly comparable
OSWorld-Verified
Claude Opus 4.7 (Adaptive)78%SourceGPT-5.3 Codex64.7%SourceClaude Opus 4.7 (Adaptive) leads this result
CyberGym
Claude Opus 4.7 (Adaptive)73.1%SourceGPT-5.3 Codex—Not directly comparable
OSWorld 2.0
Claude Opus 4.7 (Adaptive)18.2%SourceGPT-5.3 Codex—Not directly comparable
JobBench
Shared sourceClaude Opus 4.7 (Adaptive)45.9%GPT-5.3 Codex33.7%Claude Opus 4.7 (Adaptive) leads this result
Gert Labs
Claude Opus 4.7 (Adaptive)—GPT-5.3 Codex57.47%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.7 (Adaptive)87.6%SourceGPT-5.3 Codex85%SourceClaude Opus 4.7 (Adaptive) leads this result
SWE-bench Pro
Claude Opus 4.7 (Adaptive)64.3%SourceGPT-5.3 Codex56.8%SourceClaude Opus 4.7 (Adaptive) leads this result
Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%SourceGPT-5.3 Codex—Not directly comparable
SWE-Rebench
Claude Opus 4.7 (Adaptive)—GPT-5.3 Codex58.2%SourceNot directly comparable
Vibe Code Bench
Claude Opus 4.7 (Adaptive)—GPT-5.3 Codex61.77%SourceNot directly comparable
LiveCodeBench (Vals)
Claude Opus 4.7 (Adaptive)—GPT-5.3 Codex87.3%SourceNot directly comparable
SWE-bench (Vals)
Claude Opus 4.7 (Adaptive)—GPT-5.3 Codex78.0%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
Claude Opus 4.7 (Adaptive)94.2%SourceGPT-5.3 Codex—Not directly comparable
GPQA-D
Claude Opus 4.7 (Adaptive)94.2%SourceGPT-5.3 Codex—Not directly comparable
HLE
Claude Opus 4.7 (Adaptive)54.7%SourceGPT-5.3 Codex—Not directly comparable
HLE w/o tools
Claude Opus 4.7 (Adaptive)46.9%SourceGPT-5.3 Codex—Not directly comparable
Math
FrontierMath (legacy)
Claude Opus 4.7 (Adaptive)43.8%SourceGPT-5.3 Codex—Not directly comparable
26 public results · 5 shared
Watch Claude Opus 4.7 (Adaptive) vs GPT-5.3 Codex
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated September 29, 2026