Coding
Like-for-like- Claude Fable 5
- 76.9
- Supported · #2/183
- GPT-5.3 Codex
- 62.7
- Supported · #16/183
- Basis
- BenchAlign lane · 10 vs 4 public rows
- Reading
- Claude Fable 5 leads · intervals overlap
Claude Fable 5 vs GPT-5.3 Codex
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Fable 5 has the higher public score, 80.9 versus 65.22, and the 90% score intervals do not overlap.
4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Fable 5
Claude Fable 5 leads on the public coding lane, 76.9 to 62.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Claude Fable 5
Claude Fable 5 has the larger documented context window.
Confidence: documented
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-rates
Show secondary and unsupported calls
Cache-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. GPT-5.3 Codex 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-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
14 Claude Fable 5 only
4 Shared
4 GPT-5.3 Codex only
- Shared results
- 4
- Claude Fable 5 only
- 14
- GPT-5.3 Codex only
- 4
- Like-for-like categories
- 1 / 8
3 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 Fable 5
- 74.8
- Supported · #3/151
- GPT-5.3 Codex
- 62.3
- Estimated · #19/151
- Basis
- BenchAlign lane · 4 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Fable 5
- 83.5
- Supported · #2/181
- GPT-5.3 Codex
- 66.3
- Estimated · #22/181
- Basis
- BenchAlign lane · 2 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Claude Fable 5
- 78.3
- #54/120
- GPT-5.3 Codex
- 92.3
- #15/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Fable 5
- 76.2
- #11/22
- GPT-5.3 Codex
- 77.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5
- 62.5
- Unranked · 2 rankable rows
- GPT-5.3 Codex
- 75.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5 | GPT-5.3 Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 76.9Supported · #2/183 | 62.7Supported · #16/183 | Like-for-likeBenchAlign lane · 10 vs 4 public rows | Claude Fable 5 leads · intervals overlap |
| Agentic | 74.8Supported · #3/151 | 62.3Estimated · #19/151 | Directional onlyBenchAlign lane · 4 vs 4 public rows | Directional only |
| Knowledge | 83.5Supported · #2/181 | 66.3Estimated · #22/181 | Directional onlyBenchAlign lane · 2 vs 0 public rows | Directional only |
| Instruction following | 78.3#54/120 | 92.3#15/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 76.2#11/22 | 77.3Unranked · 2 rankable rows | 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 |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 62.5Unranked · 2 rankable rows | 75.6Unranked · 1 rankable row | Not comparableProvisional lane · 1 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.
SWE-bench Pro
Coding
Claude Fable 5: 80%GPT-5.3 Codex: 56.8%Normalized gap 23.2Claude Fable 5 source GPT-5.3 Codex sourceOSWorld-Verified
Agentic
Claude Fable 5: 85%GPT-5.3 Codex: 64.7%Normalized gap 20.3Claude Fable 5 source GPT-5.3 Codex sourceSWE-bench Verified
Coding
Terminal-Bench 2.0
Agentic
Claude Fable 5: 84.3%GPT-5.3 Codex: 77.3%Normalized gap 7.0Claude Fable 5 source GPT-5.3 Codex 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 Fable 5
- $0.035
- 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 Fable 5
- $0.65
- 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 Fable 5
- $0.9
- Fits in one request
- 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
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Fable 5GPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5
GPT-5.3 Codex
API model ID
Claude Fable 5
claude-fable-5
Anthropic model overviewGPT-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 Fable 5
$1 per 1M cached input tokens
Claude API pricingGPT-5.3 Codex
Not published
Documented inputs
Claude Fable 5
text, image
Anthropic model overviewGPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
Claude Fable 5
GPT-5.3 Codex
Provider availability
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewGPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Fable 5
Reasoning
GPT-5.3 Codex
Reasoning
Weight access
Claude Fable 5
Proprietary
GPT-5.3 Codex
Proprietary
License
Claude Fable 5
Proprietary
GPT-5.3 Codex
Proprietary
Release date
Claude Fable 5
2026-06-09
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 Fable 5 has the higher public score, 80.9 versus 65.22, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.65 vs $0.1295. Cache-heavy agent loop: $0.9 vs $0.525.
- Context tradeoff
- Claude Fable 5 has the larger documented window (1M).
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 evidence22 rows
Agentic
Terminal-Bench 3.0
Claude Fable 534.0%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 2.0
Claude Fable 584.3%SourceGPT-5.3 Codex77.3%SourceClaude Fable 5 leads this result
OSWorld-Verified
Claude Fable 585%SourceGPT-5.3 Codex64.7%SourceClaude Fable 5 leads this result
Terminal-Bench 2.1 (Vals)
Claude Fable 580.5%SourceGPT-5.3 Codex—Not directly comparable
Gert Labs
Claude Fable 5—GPT-5.3 Codex57.47%SourceNot directly comparable
JobBench
Claude Fable 5—GPT-5.3 Codex33.7%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Fable 595%SourceGPT-5.3 Codex85%SourceClaude Fable 5 leads this result
SWE-bench Pro
Claude Fable 580%SourceGPT-5.3 Codex56.8%SourceClaude Fable 5 leads this result
FrontierSWE v2
Claude Fable 548.0%SourceGPT-5.3 Codex—Not directly comparable
FrontierCode 1.1 Main
Claude Fable 553.5%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 2.0
Claude Fable 584.3%SourceGPT-5.3 Codex—Not directly comparable
cursorBench31
Claude Fable 570.6%SourceGPT-5.3 Codex—Not directly comparable
cursorBench32
Claude Fable 570.5%SourceGPT-5.3 Codex—Not directly comparable
VulcanBench v3
Claude Fable 589.5%SourceGPT-5.3 Codex—Not directly comparable
LiveCodeBench (Vals)
Claude Fable 589.8%SourceGPT-5.3 Codex—Not directly comparable
SWE-bench (Vals)
Claude Fable 595.0%SourceGPT-5.3 Codex—Not directly comparable
SWE-Rebench
Claude Fable 5—GPT-5.3 Codex58.2%SourceNot directly comparable
Vibe Code Bench
Claude Fable 5—GPT-5.3 Codex61.77%SourceNot directly comparable
Knowledge
Multimodal
Frequently asked questions
Which is better, Claude Fable 5 or GPT-5.3 Codex?
Claude Fable 5 has the higher public score, 80.9 versus 65.22, 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 Fable 5 or GPT-5.3 Codex?
Claude Fable 5 leads the public coding lane, 76.9 to 62.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Fable 5 or GPT-5.3 Codex?
Claude Fable 5 scores higher for agentic tasks on the public lane, 74.8 to 62.3. 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 Fable 5 or GPT-5.3 Codex?
For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.00875 on GPT-5.3 Codex; repository review costs $0.65 and $0.1295; the cache-heavy agent loop costs $0.9 and $0.525. 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 Fable 5 or GPT-5.3 Codex?
Claude Fable 5 has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 4, 2026
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