Agentic
Like-for-like- Claude Sonnet 5
- 81.9
- GPT-5.4
- 77.2
- Weighted basis
- 3 vs 3 rows
- Reading
- Claude Sonnet 5 leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start free briefUpdated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 has the higher public score estimate, 73.36 versus 64.78, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
8 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 leads on the same 3 weighted benchmark rows.
Confidence: stronger
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | Claude Sonnet 5 | GPT-5.4 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 81.9 | 77.2 | Like-for-like3 vs 3 rows | Claude Sonnet 5 leads |
| Coding | 76.7 | 57.7 | Directional only2 vs 1 rows | Directional only |
| Knowledge | 57.4 | 57.6 | Directional only1 vs 2 rows | Directional only |
| Multimodal | 88.3 | 73.2 | Directional only1 vs 3 rows | Directional only |
| Reasoning | Not measured | 74.0 | Not comparable0 vs 1 rows | Not comparable |
| Math | Not measured | 42.5 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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
CharXiv
Multimodal
SWE-bench Pro
Coding
Terminal-Bench 2.0
Agentic
HLE
Knowledge
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.
1K fresh input + 500 output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
GPT-5.4
1.05M
OpenAI pricingClaude Sonnet 5
claude-sonnet-5
Anthropic model overviewGPT-5.4
gpt-5.4
OpenAI pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingGPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingClaude Sonnet 5
text, image
Anthropic model overviewGPT-5.4
Not sourced
Claude Sonnet 5
GPT-5.4
Not sourced
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGPT-5.4
Not sourced
Claude Sonnet 5
Reasoning
GPT-5.4
Reasoning
Claude Sonnet 5
Proprietary
GPT-5.4
Proprietary
Claude Sonnet 5
Proprietary
GPT-5.4
Proprietary
Claude Sonnet 5
2026-06-30
GPT-5.4
2026-03-05
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 5 leads this result
BrowseComp
Claude Sonnet 5 leads this result
HLE w/ tools
Not directly comparable
OSWorld-Verified
Claude Sonnet 5 leads this result
CyberGym
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
ExploitGym
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Claude Sonnet 5 leads this result
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
cursorBench32
Not directly comparable
APEX-SWE
Not directly comparable
EEBench
Shared sourceClaude Sonnet 5 leads this result
3DCodeBench
Not directly comparable
LiveCodeBench Pro
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Not directly comparable
HLE
Claude Sonnet 5 leads this result
HLE w/o tools
Claude Sonnet 5 leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
HealthBench Professional
Not directly comparable
CharXiv
Claude Sonnet 5 leads this result
CharXiv w/o tools
Not directly comparable
MMMU-Pro
Not directly comparable
OfficeQA Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
ZeroBench
Not directly comparable
MedXpertQA (MM)
Not directly comparable
GPT-5.4 has the higher public score estimate, 73.36 versus 64.78, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Claude Sonnet 5 leads the like-for-like agentic tasks comparison across 3 shared weighted benchmark rows.
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.01 on GPT-5.4; repository review costs $0.13 and $0.17; the cache-heavy agent loop costs $0.18 and $0.25. Costs use the listed standard API rates.
GPT-5.4 has the larger documented context window: 1.05M, compared with 1M.
Last updated August 14, 2026
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