Agentic
Like-for-like- Claude Sonnet 4.5
- 55.4
- GPT-5.4 nano
- 42.9
- Weighted basis
- 2 vs 2 rows
- Reading
- Claude Sonnet 4.5 leads
Model comparison
Claude Sonnet 4.5 vs GPT-5.4 nano
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.4 nano has the higher public score estimate, 65.98 versus 52.82, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 4.5
Claude Sonnet 4.5 leads on the same 2 weighted benchmark rows.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.4 nano
GPT-5.4 nano has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano 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-5.4 nano
GPT-5.4 nano 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
No shared weighted benchmark basis supports a winner.
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 Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 Claude Sonnet 4.5 only
5 Shared
8 GPT-5.4 nano only
- Shared results
- 5
- Claude Sonnet 4.5 only
- 6
- GPT-5.4 nano only
- 8
- Like-for-like categories
- 2 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Math
Like-for-like- Claude Sonnet 4.5
- 11.2
- GPT-5.4 nano
- 21.0
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.4 nano leads
Knowledge
Directional only- Claude Sonnet 4.5
- 83.4
- GPT-5.4 nano
- 43.8
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Coding
Not comparable- Claude Sonnet 4.5
- 77.2
- GPT-5.4 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Sonnet 4.5
- 13.6
- GPT-5.4 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 4.5
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 4.5
- Not measured
- GPT-5.4 nano
- 66.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 4.5
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Sonnet 4.5 | GPT-5.4 nano | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 55.4 | 42.9 | Like-for-like2 vs 2 rows | Claude Sonnet 4.5 leads |
| Math | 11.2 | 21.0 | Like-for-like2 vs 2 rows | GPT-5.4 nano leads |
| Knowledge | 83.4 | 43.8 | Directional only1 vs 2 rows | Directional only |
| Coding | 77.2 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 13.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 66.1 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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-Verified
Agentic
Claude Sonnet 4.5: 61.4%GPT-5.4 nano: 39%Normalized gap 22.4Claude Sonnet 4.5 source GPT-5.4 nano sourceFrontierMath v2 (Tiers 1-3)
Math
Terminal-Bench 2.0
Agentic
Claude Sonnet 4.5: 50%GPT-5.4 nano: 46.3%Normalized gap 3.7Claude Sonnet 4.5 source GPT-5.4 nano sourceFrontierMath v2 (Tier 4)
Math
GPQA
Knowledge
Claude Sonnet 4.5: 83.4%GPT-5.4 nano: 82.8%Normalized gap 0.6Claude Sonnet 4.5 source GPT-5.4 nano 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 Sonnet 4.5
- $0.0105
- Fits in one request
- GPT-5.4 nano
- $0.00082
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 4.5
- $0.195
- Fits in one request
- GPT-5.4 nano
- $0.01375
- Fits in one request
GPT-5.4 nano 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 Sonnet 4.5
- $0.81
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.4 nano
- $0.0205
- Fits in one request
Claude Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 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 Sonnet 4.5GPT-5.4 nano
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 4.5
200K
GPT-5.4 nano
API model ID
Claude Sonnet 4.5
Not sourced
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.5
Not published
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Sonnet 4.5
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogDocumented outputs
Claude Sonnet 4.5
Not sourced
GPT-5.4 nano
Provider availability
Claude Sonnet 4.5
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Sonnet 4.5
Non-Reasoning
GPT-5.4 nano
Reasoning
Weight access
Claude Sonnet 4.5
Proprietary
GPT-5.4 nano
Proprietary
License
Claude Sonnet 4.5
Proprietary
GPT-5.4 nano
Proprietary
Release date
Claude Sonnet 4.5
2025-09-01
GPT-5.4 nano
2026-03-17
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-5.4 nano has the higher public score estimate, 65.98 versus 52.82, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.195 vs $0.01375. Cache-heavy agent loop: $0.81 vs $0.0205.
- Context tradeoff
- GPT-5.4 nano has the larger documented window (400K).
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 evidence19 rows
Agentic
Terminal-Bench 2.0
Claude Sonnet 4.550%SourceGPT-5.4 nano46.3%SourceClaude Sonnet 4.5 leads this result
OSWorld-Verified
Claude Sonnet 4.561.4%SourceGPT-5.4 nano39%SourceClaude Sonnet 4.5 leads this result
VITA-Bench
Claude Sonnet 4.517.0%SourceGPT-5.4 nano—Not directly comparable
Gert Labs
Claude Sonnet 4.548.51%SourceGPT-5.4 nano—Not directly comparable
JobBench
Claude Sonnet 4.527.7%SourceGPT-5.4 nano—Not directly comparable
MCP Atlas
Claude Sonnet 4.5—GPT-5.4 nano56.1%SourceNot directly comparable
Toolathlon
Claude Sonnet 4.5—GPT-5.4 nano35.5%SourceNot directly comparable
τ²-bench results
Claude Sonnet 4.5—GPT-5.4 nano92.5%SourceNot directly comparable
Coding
Reasoning
ARC-AGI-2
Claude Sonnet 4.513.6%SourceGPT-5.4 nano—Not directly comparable
Knowledge
Math
AIME 2025
Claude Sonnet 4.587%SourceGPT-5.4 nano—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Sonnet 4.513.495%GPT-5.4 nano25.860%GPT-5.4 nano leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Sonnet 4.54.167%GPT-5.4 nano6.250%GPT-5.4 nano leads this result
Multimodal
Frequently asked questions
Which is better, Claude Sonnet 4.5 or GPT-5.4 nano?
GPT-5.4 nano has the higher public score estimate, 65.98 versus 52.82, 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 Sonnet 4.5 or GPT-5.4 nano?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Claude Sonnet 4.5 or GPT-5.4 nano?
Claude Sonnet 4.5 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, Claude Sonnet 4.5 or GPT-5.4 nano?
For the stated presets, chat costs $0.0105 on Claude Sonnet 4.5 and $0.00082 on GPT-5.4 nano; repository review costs $0.195 and $0.01375; the cache-heavy agent loop costs $0.81 and $0.0205. Claude Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Sonnet 4.5 or GPT-5.4 nano?
GPT-5.4 nano has the larger documented context window: 400K, compared with 200K.
Related comparisons
Last updated July 30, 2026
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