Agentic
Not comparable- Step 3.7 Flash
- 66.4
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Step 3.7 Flash vs Trinity-Large-Thinking
Updated August 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Step 3.7 Flash has the higher public score estimate, 51.05 versus 48.17, 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
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
Step 3.7 Flash
Step 3.7 Flash has the lower estimated token cost for this stated workload. Step 3.7 Flash 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
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Step 3.7 Flash
Step 3.7 Flash 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.
10 Step 3.7 Flash only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- Step 3.7 Flash only
- 10
- 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- Step 3.7 Flash
- 56.3
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Step 3.7 Flash
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Step 3.7 Flash
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Step 3.7 Flash
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Step 3.7 Flash
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Step 3.7 Flash
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Step 3.7 Flash
- Not measured
- Trinity-Large-Thinking
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Step 3.7 Flash | Trinity-Large-Thinking | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 66.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 56.3 | Not measured | Not comparable1 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
- Step 3.7 Flash
- $0.00077
- 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
- Step 3.7 Flash
- $0.01345
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Step 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Step 3.7 Flash
- $0.0555
- 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
Step 3.7 Flash has the lower modeled cost
Step 3.7 Flash 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.
SpecificationStep 3.7 FlashTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Step 3.7 Flash
256K
Trinity-Large-Thinking
512K
API model ID
Step 3.7 Flash
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.
Step 3.7 Flash
Not published
Trinity-Large-Thinking
Not published
Documented inputs
Step 3.7 Flash
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
Step 3.7 Flash
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
Step 3.7 Flash
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
Step 3.7 Flash
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
Step 3.7 Flash
Open Weight
Trinity-Large-Thinking
Open Weight
License
Step 3.7 Flash
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
Step 3.7 Flash
2026-05-29
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
- Step 3.7 Flash has the higher public score estimate, 51.05 versus 48.17, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.01345 vs $0.0152. Cache-heavy agent loop: $0.0555 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 evidence15 rows
Agentic
Terminal-Bench 2.0
Step 3.7 Flash59.5%SourceTrinity-Large-Thinking—Not directly comparable
BrowseComp
Step 3.7 Flash75.8%SourceTrinity-Large-Thinking—Not directly comparable
DeepSearchQA
Step 3.7 Flash92.8%SourceTrinity-Large-Thinking—Not directly comparable
Toolathlon
Step 3.7 Flash49.5%SourceTrinity-Large-Thinking—Not directly comparable
Claw-Eval
Step 3.7 Flash67.1%SourceTrinity-Large-Thinking—Not directly comparable
HLE w/ tools
Step 3.7 Flash47.2%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
Shared sourceStep 3.7 Flash51.57%Trinity-Large-Thinking32.55%Step 3.7 Flash leads this result
Coding
Knowledge
Math
AIME25 (Arcee)
Step 3.7 Flash—Trinity-Large-Thinking96.3%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, Step 3.7 Flash or Trinity-Large-Thinking?
Step 3.7 Flash has the higher public score estimate, 51.05 versus 48.17, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Step 3.7 Flash 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, Step 3.7 Flash 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, Step 3.7 Flash or Trinity-Large-Thinking?
For the stated presets, chat costs $0.00077 on Step 3.7 Flash and $0.0007 on Trinity-Large-Thinking; repository review costs $0.01345 and $0.0152; the cache-heavy agent loop costs $0.0555 and $0.064. Step 3.7 Flash 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, Step 3.7 Flash or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 256K.
Related comparisons
Last updated August 30, 2026
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