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.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 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 15.6, 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.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
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.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Mistral Large 3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.
3 categories rest 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 | GPT-5.4 nano | Mistral Large 3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 30.1Supported · #100/156 | 15.6Supported · #146/156 | Like-for-likeBenchAlign v5.8 lane · 4 vs 0 public rows | GPT-5.4 nano leads · intervals overlap |
| Agentic | 33.7Supported · #97/135 | 15.7Estimated · #129/135 | Directional onlyBenchAlign v5.8 lane · 6 vs 0 public rows | Directional only |
| Knowledge | 39.4Supported · #117/179 | 30.6Estimated · #149/179 | Directional onlyBenchAlign v5.8 lane · 5 vs 0 public rows | Directional only |
| Instruction following | 93.3#12/157 | 41.5#122/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 40.6Unranked · 4 rankable rows | 49.7Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 52.6#51/58 | 47.3Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 52.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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
GPT-5.4 nano has the lower modeled cost
Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
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.
GPT-5.4 nano
Mistral Large 3
256K
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationMistral Large 3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingMistral Large 3
Not published
GPT-5.4 nano
text, image
OpenAI model catalogMistral Large 3
Not sourced
GPT-5.4 nano
Mistral Large 3
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogMistral Large 3
Not sourced
GPT-5.4 nano
Reasoning
Mistral Large 3
Non-Reasoning
GPT-5.4 nano
Proprietary
Mistral Large 3
Proprietary
GPT-5.4 nano
Proprietary
Mistral Large 3
Proprietary
GPT-5.4 nano
2026-03-17
Mistral Large 3
2025-12-02
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. 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 15.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.4 nano scores higher for agentic tasks on the public lane, 33.7 to 15.7. Mistral Large 3 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.
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.00125 on Mistral Large 3; repository review costs $0.01375 and $0.0295; the cache-heavy agent loop costs $0.0205 and $0.125. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
GPT-5.4 nano has the larger documented context window: 400K, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
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
Terminal-Bench 2.1 (Vals)
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated October 29, 2026
