Updated October 17, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.4 has the higher public point estimate, 66.7 versus 41.16. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or 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.
Code generation, repair, and software-engineering tasks
GPT-5.4
GPT-5.4 has the higher public coding point estimate, 47.7 to 16.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-5.4
GPT-5.4 has the higher public agentic point estimate, 52.1 to 21.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
1K fresh input + 500 output tokens
Claude Haiku 4.5
Claude Haiku 4.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Haiku 4.5
Claude Haiku 4.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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 Haiku 4.5 does not fit this workload in one request.
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.
Like-for-like · BenchAlign v5.8
GPT-5.4 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Claude Haiku 4.5 | GPT-5.4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 21.4Supported · #107/128 | 52.1Supported · #44/128 | Like-for-likeBenchAlign v5.8 lane · 3 vs 15 public rows | GPT-5.4 leads |
| Coding | 16.8Supported · #126/150 | 47.7Supported · #48/150 | Like-for-likeBenchAlign v5.8 lane · 4 vs 6 public rows | GPT-5.4 leads |
| Knowledge | 33.3Estimated · #141/184 | 69.1Supported · #20/184 | Directional onlyBenchAlign v5.8 lane · 2 vs 7 public rows | Directional only |
| Reasoning | Not ranked | 64.2#23/28 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | Not ranked | 73.1#30/54 | Not comparableProvisional lane · 0 vs 3 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 90.7#23/148 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 82.0Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.
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 Haiku 4.5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Haiku 4.5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Haiku 4.5 does not fit this workload in one request.
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 Haiku 4.5
GPT-5.4
1.05M
OpenAI pricingClaude Haiku 4.5
claude-haiku-4-5-20251001
Claude API pricingGPT-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 Haiku 4.5
$0.1 per 1M cached input tokens
Claude API pricingGPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingClaude Haiku 4.5
Not sourced
GPT-5.4
Not sourced
Claude Haiku 4.5
Not sourced
GPT-5.4
Not sourced
Claude Haiku 4.5
Not sourced
GPT-5.4
Not sourced
Claude Haiku 4.5
Non-Reasoning
GPT-5.4
Reasoning
Claude Haiku 4.5
Proprietary
GPT-5.4
Proprietary
Claude Haiku 4.5
Proprietary
GPT-5.4
Proprietary
Claude Haiku 4.5
2025-10-15
GPT-5.4
2026-03-05
GPT-5.4 has the higher public point estimate, 66.7 versus 41.16. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.4 has the higher public coding point estimate, 47.7 to 16.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.4 has the higher public agentic tasks point estimate, 52.1 to 21.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0035 on Claude Haiku 4.5 and $0.01 on GPT-5.4; repository review costs $0.065 and $0.17; the cache-heavy agent loop costs $0.09 and $0.25. Claude Haiku 4.5 does not fit this workload in one request.
GPT-5.4 has the larger documented context window: 1.05M, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
JobBench
Shared sourceGPT-5.4 leads this result
Terminal-Bench 2.1 (Vals)
Not directly comparable
MazeBench
Tie
Terminal-Bench 2.0
Not directly comparable
CyberGym
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
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
ExploitGym
Not directly comparable
ApprenticeBench
Not directly comparable
SWE-bench Verified
Not directly comparable
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
LiveCodeBench Pro
Not directly comparable
SWE-bench Pro
Not directly comparable
React Native Evals
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
PostTrainBench v1.1
Not directly comparable
MMMU-Pro
Not directly comparable
OfficeQA Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
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
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
HealthBench Professional
Not directly comparable
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Last updated October 17, 2026
