Updated October 29, 2026. Rank says GPT-5.4 nano is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.4 nano has the higher public point estimate, 46.15 versus 39.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 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 nano
GPT-5.4 nano has the higher public coding point estimate, 30.1 to 17, 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 nano
GPT-5.4 nano has the higher public agentic point estimate, 33.7 to 22.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.4 nano
GPT-5.4 nano has the larger documented context window.
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.
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.
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 nano 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.
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.
LiveCodeBench (Vals)Coding
Normalized gap 42.8MMLU-Pro (Vals)Knowledge
Normalized gap 1.5Each 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 nano | Basis | Reading |
|---|---|---|---|---|
| Agentic | 22.3Supported · #121/135 | 33.7Supported · #97/135 | Like-for-likeBenchAlign v5.8 lane · 4 vs 6 public rows | GPT-5.4 nano leads · intervals overlap |
| Coding | 17.0Supported · #142/156 | 30.1Supported · #100/156 | Like-for-likeBenchAlign v5.8 lane · 4 vs 4 public rows | GPT-5.4 nano leads · intervals overlap |
| Knowledge | 31.5Estimated · #145/179 | 39.4Supported · #117/179 | Directional onlyBenchAlign v5.8 lane · 2 vs 5 public rows | Directional only |
| Reasoning | Not ranked | 40.6Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 52.6#51/58 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.3#12/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 52.3Unranked · 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
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano 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 nano
Claude Haiku 4.5
claude-haiku-4-5-20251001
Claude API pricingGPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationA 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 nano
$0.02 per 1M cached input tokens
OpenAI pricingClaude Haiku 4.5
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogClaude Haiku 4.5
Not sourced
GPT-5.4 nano
Claude Haiku 4.5
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogClaude Haiku 4.5
Non-Reasoning
GPT-5.4 nano
Reasoning
Claude Haiku 4.5
Proprietary
GPT-5.4 nano
Proprietary
Claude Haiku 4.5
Proprietary
GPT-5.4 nano
Proprietary
Claude Haiku 4.5
2025-10-15
GPT-5.4 nano
2026-03-17
GPT-5.4 nano has the higher public point estimate, 46.15 versus 39.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.4 nano has the higher public coding point estimate, 30.1 to 17, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.4 nano has the higher public agentic tasks point estimate, 33.7 to 22.3, 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 $0.00082 on GPT-5.4 nano; repository review costs $0.065 and $0.01375; the cache-heavy agent loop costs $0.09 and $0.0205. Claude Haiku 4.5 does not fit this workload in one request.
GPT-5.4 nano has the larger documented context window: 400K, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
JobBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Haiku 4.5 leads this result
MazeBench
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
SWE-bench Verified
Not directly comparable
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano leads this result
SWE-bench (Vals)
GPT-5.4 nano leads this result
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
ParseBench
Shared sourceClaude Haiku 4.5 leads this result
ExtractBench (LlamaIndex)
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 nano leads this result
MMLU-Pro (Vals)
Claude Haiku 4.5 leads this result
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
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Last updated October 29, 2026
