Coding work
Code generation, repair, and software-engineering tasks
GPT-5.4 nano
GPT-5.4 nano leads on the public coding lane, 31.2 to 21, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-5.4 nano vs Trinity-Large-Thinking
Updated September 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.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
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.
- Shared results
- 0
- GPT-5.4 nano only
- 20
- Trinity-Large-Thinking only
- 5
- Like-for-like categories
- 1 / 8
Supported: GPT-5.4 nano · Estimated: Trinity-Large-ThinkingHow the comparison works
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
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. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallbackRepository review cost
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.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Trinity-Large-Thinking is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Which one for a specific job
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.
31.2GPT-5.4 nano21.0Trinity-Large-Thinking
Like-for-like · BenchAlign v5.7
GPT-5.4 nano leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
20 GPT-5.4 nano only
5 Trinity-Large-Thinking only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Coding
Like-for-like- GPT-5.4 nano
- 31.2
- Supported · #90/142
- Trinity-Large-Thinking
- 21.0
- Supported · #119/142
- Basis
- BenchAlign v5.7 lane · 3 vs 1 public rows
- Reading
- GPT-5.4 nano leads · intervals overlap
Agentic
Directional only- GPT-5.4 nano
- 31.9
- Supported · #75/117
- Trinity-Large-Thinking
- 17.7
- Estimated · #104/117
- Basis
- BenchAlign v5.7 lane · 6 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.4 nano
- 37.8
- Supported · #104/168
- Trinity-Large-Thinking
- 35.9
- Estimated · #114/168
- Basis
- BenchAlign v5.7 lane · 5 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.4 nano
- 91.9
- #9/124
- Trinity-Large-Thinking
- 66.3
- #68/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.4 nano
- 37.0
- Unranked · 4 rankable rows
- Trinity-Large-Thinking
- 48.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 24.9
- #47/50
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 43.8
- Unranked · 2 rankable rows
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 31.2Supported · #90/142 | 21.0Supported · #119/142 | Like-for-likeBenchAlign v5.7 lane · 3 vs 1 public rows | GPT-5.4 nano leads · intervals overlap |
| Agentic | 31.9Supported · #75/117 | 17.7Estimated · #104/117 | Directional onlyBenchAlign v5.7 lane · 6 vs 1 public rows | Directional only |
| Knowledge | 37.8Supported · #104/168 | 35.9Estimated · #114/168 | Directional onlyBenchAlign v5.7 lane · 5 vs 2 public rows | Directional only |
| Instruction following | 91.9#9/124 | 66.3#68/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 37.0Unranked · 4 rankable rows | 48.3Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 24.9#47/50 | Not ranked | 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 | 43.8Unranked · 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 v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- GPT-5.4 nano
- $0.00082
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.4 nano
- $0.01375
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.4 nano
- $0.0205
- Fits in one request
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.4 nano has the lower modeled cost
Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.4 nanoTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
Trinity-Large-Thinking
512K
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationTrinity-Large-Thinking
Not sourced
Cached-input rate
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 pricingTrinity-Large-Thinking
Not published
Documented inputs
GPT-5.4 nano
text, image
OpenAI model catalogTrinity-Large-Thinking
Not sourced
Documented outputs
GPT-5.4 nano
Trinity-Large-Thinking
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogTrinity-Large-Thinking
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
GPT-5.4 nano
Proprietary
Trinity-Large-Thinking
Open Weight
License
GPT-5.4 nano
Proprietary
Trinity-Large-Thinking
Open Weight
Release date
GPT-5.4 nano
2026-03-17
Trinity-Large-Thinking
2026-03-10
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.01375 vs $0.0152. Cache-heavy agent loop: $0.0205 vs $0.064.
- Context tradeoff
- Trinity-Large-Thinking has the larger documented window (512K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.4 nano or Trinity-Large-Thinking?
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.
Which is better for coding, GPT-5.4 nano or Trinity-Large-Thinking?
GPT-5.4 nano leads the public coding lane, 31.2 to 21, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.4 nano or Trinity-Large-Thinking?
GPT-5.4 nano scores higher for agentic tasks on the public lane, 31.9 to 17.7. Trinity-Large-Thinking 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.
Which costs less, GPT-5.4 nano or Trinity-Large-Thinking?
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0007 on Trinity-Large-Thinking; repository review costs $0.01375 and $0.0152; the cache-heavy agent loop costs $0.0205 and $0.064. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.4 nano or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence25 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceTrinity-Large-Thinking—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceTrinity-Large-Thinking—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%SourceTrinity-Large-Thinking—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%SourceTrinity-Large-Thinking—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
GPT-5.4 nano—Trinity-Large-Thinking32.55%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.4 nano26.10%SourceTrinity-Large-Thinking—Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano84.0%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench (Vals)
GPT-5.4 nano69.8%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
GPT-5.4 nano—Trinity-Large-Thinking63.2%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.4 nano82.8%SourceTrinity-Large-Thinking—Not directly comparable
HLE
GPT-5.4 nano37.7%SourceTrinity-Large-Thinking—Not directly comparable
HLE w/o tools
GPT-5.4 nano24.3%SourceTrinity-Large-Thinking—Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 nano77.5%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 nano77.2%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
GPT-5.4 nano—Trinity-Large-Thinking76.3%SourceNot directly comparable
MMLU-Pro (Arcee)
GPT-5.4 nano—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
25 public results · 0 shared
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Last updated September 29, 2026