Long documents
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
Confidence: documented
Solar Pro 3 vs Trinity-Large-Thinking
Updated October 25, 2026. Rank says Trinity-Large-Thinking is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
Trinity-Large-Thinking has the higher public point estimate, 34.51 versus 30.03. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model B
Arcee AI
34.51/100
Estimated · Public rank #162
Conditional range 24.8–44.2
- Shared results
- 1
- Solar Pro 3 only
- 0
- Trinity-Large-Thinking only
- 6
- Like-for-like categories
- 1 / 8
Estimated: Solar Pro 3 and Trinity-Large-Thinking. Conditional ranges do not establish rank confidence.How 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.
Chat turn cost
1K fresh input + 500 output tokens
Solar Pro 3
Solar Pro 3 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
Repository review cost
50K fresh input + 3K output tokens
Solar Pro 3
Solar Pro 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Solar Pro 3 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Solar Pro 3 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Solar Pro 3 does not fit this workload in one request. Solar Pro 3 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
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.
—Solar Pro 318.0Trinity-Large-Thinking
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
1 Shared
6 Trinity-Large-Thinking only
1 category rests 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
IFBenchInstruction following
Normalized gap 3.5Solar Pro 3:55.8%Trinity-Large-Thinking:52.3%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Instruction following
Like-for-like- Solar Pro 3
- 87.1
- #42/153
- Trinity-Large-Thinking
- 28.9
- #136/153
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Solar Pro 3 leads
Knowledge
Directional only- Solar Pro 3
- 30.0
- Estimated · #156/187
- Trinity-Large-Thinking
- 33.6
- Estimated · #140/187
- Basis
- BenchAlign v5.8 lane · 0 vs 2 public rows
- Reading
- Directional only
Agentic
Not comparable- Solar Pro 3
- Not ranked
- Trinity-Large-Thinking
- 14.1
- Estimated · #121/132
- Basis
- BenchAlign v5.8 lane · 0 vs 2 public rows
- Reading
- Not comparable
Coding
Not comparable- Solar Pro 3
- Not ranked
- Trinity-Large-Thinking
- 18.0
- Supported · #127/153
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Solar Pro 3
- 47.2
- Unranked · 2 rankable rows
- Trinity-Large-Thinking
- 51.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Solar Pro 3
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Solar Pro 3
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Solar Pro 3
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Solar Pro 3 | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 87.1#42/153 | 28.9#136/153 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Solar Pro 3 leads |
| Knowledge | 30.0Estimated · #156/187 | 33.6Estimated · #140/187 | Directional onlyBenchAlign v5.8 lane · 0 vs 2 public rows | Directional only |
| Agentic | Not ranked | 14.1Estimated · #121/132 | Not comparableBenchAlign v5.8 lane · 0 vs 2 public rows | Not comparable |
| Coding | Not ranked | 18.0Supported · #127/153 | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Reasoning | 47.2Unranked · 2 rankable rows | 51.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
- Solar Pro 3
- $0.00045
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Solar Pro 3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Solar Pro 3
- $0.0093
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Solar Pro 3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Solar Pro 3
- $0.039
- Does not fit 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
Solar Pro 3 does not fit this workload in one request. Solar Pro 3 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.
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.
SpecificationSolar Pro 3Trinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Solar Pro 3
Trinity-Large-Thinking
512K
API model ID
Solar Pro 3
solar-pro3
Upstage API pricingTrinity-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.
Solar Pro 3
Not published
Upstage API pricingTrinity-Large-Thinking
Not published
Documented inputs
Solar Pro 3
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
Solar Pro 3
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
Solar Pro 3
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
Solar Pro 3
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
Solar Pro 3
Proprietary
Trinity-Large-Thinking
Open Weight
License
Solar Pro 3
Proprietary
Trinity-Large-Thinking
Open Weight
Release date
Solar Pro 3
2026-01-26
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
- Trinity-Large-Thinking has the higher public point estimate, 34.51 versus 30.03. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.0093 vs $0.0152. Cache-heavy agent loop: $0.039 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, Solar Pro 3 or Trinity-Large-Thinking?
Trinity-Large-Thinking has the higher public point estimate, 34.51 versus 30.03. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Solar Pro 3 or Trinity-Large-Thinking?
Solar Pro 3 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Solar Pro 3 or Trinity-Large-Thinking?
Solar Pro 3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Solar Pro 3 or Trinity-Large-Thinking?
For the stated presets, chat costs $0.00045 on Solar Pro 3 and $0.0007 on Trinity-Large-Thinking; repository review costs $0.0093 and $0.0152; the cache-heavy agent loop costs $0.039 and $0.064. Solar Pro 3 does not fit this workload in one request. Solar Pro 3 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, Solar Pro 3 or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Solar Pro 3
API / mo$563
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Trinity-Large-Thinking
API / mo$863
Self-host / mo$13,981
Break-even1.0B/day
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
7 public results · 1 shared
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Last updated October 25, 2026