Math
Directional only- GPT-5.4 mini
- 21.7
- Llama 4 Maverick
- 0.7
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.4 mini has the higher public score, 55.79 versus 22.7, and the 90% score intervals do not overlap.
1 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.
Prompts that approach the documented context limit
Llama 4 Maverick
Llama 4 Maverick has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses 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 | GPT-5.4 mini | Llama 4 Maverick | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 21.7 | 0.7 | Directional only2 vs 1 rows | Directional only |
| Agentic | 65.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 47.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 76.6 | Not measured | Not comparable1 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.
FrontierMath v2 (Tiers 1-3)
Math
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
Llama 4 Maverick has no comparable published API token rate.
50K fresh input + 3K output tokens
Llama 4 Maverick has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Llama 4 Maverick has no comparable published API token 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 mini
Llama 4 Maverick
1M
GPT-5.4 mini
gpt-5.4-mini
OpenAI GPT-5.4 mini model documentationLlama 4 Maverick
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 mini
$0.075 per 1M cached input tokens
OpenAI pricingLlama 4 Maverick
No comparable hosted API rate
GPT-5.4 mini
text, image
OpenAI model catalogLlama 4 Maverick
Not sourced
GPT-5.4 mini
Llama 4 Maverick
Not sourced
GPT-5.4 mini
Generally Available · OpenAI Responses API
OpenAI model catalogLlama 4 Maverick
Not sourced
GPT-5.4 mini
Reasoning
Llama 4 Maverick
Non-Reasoning
GPT-5.4 mini
Proprietary
Llama 4 Maverick
Open Weight
GPT-5.4 mini
Proprietary
Llama 4 Maverick
Open Weight
GPT-5.4 mini
2026-03-17
Llama 4 Maverick
2026-02-28
Run the same representative tasks against both endpoints before changing production traffic.
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
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.4 mini leads this result
FrontierMath v2 (Tier 4)
Not directly comparable
GPT-5.4 mini has the higher public score, 55.79 versus 22.7, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Llama 4 Maverick has the larger documented context window: 1M, compared with 400K.
Last updated July 30, 2026
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