Coding
Like-for-like- GPT-5.2
- 47.3
- Supported · #91/183
- Trinity-Large-Thinking
- 28.1
- Supported · #172/183
- Basis
- BenchAlign lane · 3 vs 1 public rows
- Reading
- GPT-5.2 leads
GPT-5.2 vs Trinity-Large-Thinking
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.2 has the higher public score estimate, 65.45 versus 47.12, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.2
GPT-5.2 leads on the public coding lane, 47.3 to 28.1, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
Confidence: documented
Chat 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
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K 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
Agentic 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
14 GPT-5.2 only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- GPT-5.2 only
- 14
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 1 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Agentic
Directional only- GPT-5.2
- 43.0
- Supported · #108/151
- Trinity-Large-Thinking
- 41.3
- Estimated · #117/151
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.2
- 62.0
- Supported · #30/181
- Trinity-Large-Thinking
- 44.1
- Estimated · #121/181
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.2
- 92.3
- #14/120
- Trinity-Large-Thinking
- 67.5
- #64/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.2
- 53.8
- Unranked · 3 rankable rows
- Trinity-Large-Thinking
- 48.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2
- 57.5
- Unranked · 2 rankable rows
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2
- 66.3
- #23/48
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2 | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 47.3Supported · #91/183 | 28.1Supported · #172/183 | Like-for-likeBenchAlign lane · 3 vs 1 public rows | GPT-5.2 leads |
| Agentic | 43.0Supported · #108/151 | 41.3Estimated · #117/151 | Directional onlyBenchAlign lane · 4 vs 1 public rows | Directional only |
| Knowledge | 62.0Supported · #30/181 | 44.1Estimated · #121/181 | Directional onlyBenchAlign lane · 1 vs 2 public rows | Directional only |
| Instruction following | 92.3#14/120 | 67.5#64/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 53.8Unranked · 3 rankable rows | 48.6Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 57.5Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 66.3#23/48 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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-5.2
- $0.00875
- 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-5.2
- $0.1295
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Trinity-Large-Thinking 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-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Thinking has the lower modeled cost
GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.2Trinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
Trinity-Large-Thinking
512K
API model ID
GPT-5.2
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-5.2
Not published
Trinity-Large-Thinking
Not published
Documented inputs
GPT-5.2
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
GPT-5.2
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
GPT-5.2
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
GPT-5.2
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
GPT-5.2
Proprietary
Trinity-Large-Thinking
Open Weight
License
GPT-5.2
Proprietary
Trinity-Large-Thinking
Open Weight
Release date
GPT-5.2
2025-12-11
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-5.2 has the higher public score estimate, 65.45 versus 47.12, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.1295 vs $0.0152. Cache-heavy agent loop: $0.525 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence19 rows
Agentic
BrowseComp
GPT-5.265.8%SourceTrinity-Large-Thinking—Not directly comparable
OSWorld-Verified
GPT-5.247.3%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
Shared sourceGPT-5.246.54%Trinity-Large-Thinking32.55%GPT-5.2 leads this result
JobBench
GPT-5.234.3%SourceTrinity-Large-Thinking—Not directly comparable
Coding
SWE-bench Verified
GPT-5.280%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Pro
GPT-5.255.6%SourceTrinity-Large-Thinking—Not directly comparable
Vibe Code Bench
GPT-5.253.50%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
GPT-5.2—Trinity-Large-Thinking63.2%SourceNot directly comparable
Reasoning
ARC-AGI-2
GPT-5.252.9%SourceTrinity-Large-Thinking—Not directly comparable
Knowledge
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.2 or Trinity-Large-Thinking?
GPT-5.2 has the higher public score estimate, 65.45 versus 47.12, 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-5.2 or Trinity-Large-Thinking?
GPT-5.2 leads the public coding lane, 47.3 to 28.1, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.2 or Trinity-Large-Thinking?
GPT-5.2 scores higher for agentic tasks on the public lane, 43 to 41.3. 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-5.2 or Trinity-Large-Thinking?
For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.0007 on Trinity-Large-Thinking; repository review costs $0.1295 and $0.0152; the cache-heavy agent loop costs $0.525 and $0.064. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.2 or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 400K.
Related comparisons
Last updated September 4, 2026
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