Agentic
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
GPT-5.1-Codex vs Trinity-Large-Thinking
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.1-Codex has the higher public score estimate, 51.79 versus 47.46, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Chat turn cost
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
Cache-heavy agent loop cost
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: rate-fallback
Repository review cost
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 GPT-5.1-Codex only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- GPT-5.1-Codex only
- 2
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
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.
Coding
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.1-Codex
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.1-Codex | Trinity-Large-Thinking | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 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 | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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-5.1-Codex
- API rate not published
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
GPT-5.1-Codex has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.1-Codex
- API rate not published
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
GPT-5.1-Codex has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.1-Codex
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.1-Codex has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.1-CodexTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1-Codex
400K
Trinity-Large-Thinking
512K
API model ID
GPT-5.1-Codex
Not sourced
Trinity-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-5.1-Codex
No comparable hosted API rate
Trinity-Large-Thinking
Not published
Documented inputs
GPT-5.1-Codex
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
GPT-5.1-Codex
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
GPT-5.1-Codex
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
GPT-5.1-Codex
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
GPT-5.1-Codex
Proprietary
Trinity-Large-Thinking
Open Weight
License
GPT-5.1-Codex
Proprietary
Trinity-Large-Thinking
Open Weight
Release date
GPT-5.1-Codex
2025-10-15
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
- GPT-5.1-Codex has the higher public score estimate, 51.79 versus 47.46, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Trinity-Large-Thinking has the larger documented window (512K).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence7 rows
Agentic
Gert Labs
Shared sourceGPT-5.1-Codex49.68%Trinity-Large-Thinking32.55%GPT-5.1-Codex leads this result
JobBench
GPT-5.1-Codex26.2%SourceTrinity-Large-Thinking—Not directly comparable
Coding
Knowledge
Math
AIME25 (Arcee)
GPT-5.1-Codex—Trinity-Large-Thinking96.3%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.1-Codex or Trinity-Large-Thinking?
GPT-5.1-Codex has the higher public score estimate, 51.79 versus 47.46, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.1-Codex or Trinity-Large-Thinking?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.1-Codex or Trinity-Large-Thinking?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.1-Codex or Trinity-Large-Thinking?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-5.1-Codex or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 400K.
Related comparisons
Last updated August 10, 2026
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