Agentic
Not comparable- GPT-5.6 Sol
- 92.0
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Model comparison
GPT-5.6 Sol vs Trinity-Large-Thinking
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Sol has the higher public score, 81.48 versus 47.46, 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.
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
GPT-5.6 Sol
GPT-5.6 Sol 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
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. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
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
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
23 GPT-5.6 Sol only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- GPT-5.6 Sol only
- 23
- 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-5.6 Sol
- 64.6
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Sol
- 92.5
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Sol
- 94.6
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 87.5
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Sol
- 83.0
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Trinity-Large-Thinking | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 92.0 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 64.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 92.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 94.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 87.5 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 83.0 | Not measured | Not comparable1 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-5.6 Sol
- $0.02
- 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-5.6 Sol
- $0.34
- 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-5.6 Sol
- $0.5
- Fits in one request
- 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
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-5.6 SolTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogTrinity-Large-Thinking
512K
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogTrinity-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-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingTrinity-Large-Thinking
Not published
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogTrinity-Large-Thinking
Not sourced
Documented outputs
GPT-5.6 Sol
Trinity-Large-Thinking
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogTrinity-Large-Thinking
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Trinity-Large-Thinking
Open Weight
License
GPT-5.6 Sol
Proprietary
Trinity-Large-Thinking
Open Weight
Release date
GPT-5.6 Sol
2026-07-09
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-5.6 Sol has the higher public score, 81.48 versus 47.46, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.34 vs $0.0152. Cache-heavy agent loop: $0.5 vs $0.064.
- Context tradeoff
- GPT-5.6 Sol has the larger documented window (1.05M).
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 evidence28 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceTrinity-Large-Thinking—Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%SourceTrinity-Large-Thinking—Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%SourceTrinity-Large-Thinking—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceTrinity-Large-Thinking—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceTrinity-Large-Thinking—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
GPT-5.6 Sol—Trinity-Large-Thinking32.55%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Sol64.6%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceTrinity-Large-Thinking—Not directly comparable
deepSwe
GPT-5.6 Sol72.7%SourceTrinity-Large-Thinking—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceTrinity-Large-Thinking—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceTrinity-Large-Thinking—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
GPT-5.6 Sol—Trinity-Large-Thinking63.2%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
GPT-5.6 Sol94.6%SourceTrinity-Large-Thinking76.3%SourceGPT-5.6 Sol leads this result
HealthBench Professional
GPT-5.6 Sol60.5%SourceTrinity-Large-Thinking—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Pro (Arcee)
GPT-5.6 Sol—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Sol89%SourceTrinity-Large-Thinking—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%SourceTrinity-Large-Thinking—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%SourceTrinity-Large-Thinking—Not directly comparable
AIME25 (Arcee)
GPT-5.6 Sol—Trinity-Large-Thinking96.3%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, GPT-5.6 Sol or Trinity-Large-Thinking?
GPT-5.6 Sol has the higher public score, 81.48 versus 47.46, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-5.6 Sol 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-5.6 Sol 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-5.6 Sol or Trinity-Large-Thinking?
For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.0007 on Trinity-Large-Thinking; repository review costs $0.34 and $0.0152; the cache-heavy agent loop costs $0.5 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-5.6 Sol or Trinity-Large-Thinking?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 512K.
Related comparisons
Last updated August 10, 2026
Watch GPT-5.6 Sol vs Trinity-Large-Thinking
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.