Updated September 29, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Trinity-Large-Thinking
Trinity-Large-Thinking leads on the public coding lane, 21 to 18.5, with Supported evidence for both models, although the 90% intervals overlap.
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
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.
200K cached + 20K fresh input + 10K output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Mistral Large 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.
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.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Mistral Large 3 and Trinity-Large-Thinking are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.
Like-for-like · BenchAlign v5.7
Trinity-Large-Thinking leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
| Category | Mistral Large 3 | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 18.5Supported · #132/142 | 21.0Supported · #119/142 | Like-for-likeBenchAlign v5.7 lane · 0 vs 1 public rows | Trinity-Large-Thinking leads · intervals overlap |
| Agentic | 16.9Estimated · #105/117 | 17.7Estimated · #104/117 | Directional onlyBenchAlign v5.7 lane · 0 vs 1 public rows | Directional only |
| Knowledge | 33.7Estimated · #120/168 | 35.9Estimated · #114/168 | Directional onlyBenchAlign v5.7 lane · 0 vs 2 public rows | Directional only |
| Instruction following | 40.0#101/124 | 66.3#68/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 46.9Unranked · 2 rankable rows | 48.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 43.2Unranked · 1 rankable row | 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 v5.7) 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.
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.
1K fresh input + 500 output tokens
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Trinity-Large-Thinking has the lower modeled cost
Mistral Large 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.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Mistral Large 3
256K
Trinity-Large-Thinking
512K
Mistral Large 3
Not sourced
Trinity-Large-Thinking
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mistral Large 3
Not published
Trinity-Large-Thinking
Not published
Mistral Large 3
Not sourced
Trinity-Large-Thinking
Not sourced
Mistral Large 3
Not sourced
Trinity-Large-Thinking
Not sourced
Mistral Large 3
Not sourced
Trinity-Large-Thinking
Not sourced
Mistral Large 3
Non-Reasoning
Trinity-Large-Thinking
Reasoning
Mistral Large 3
Proprietary
Trinity-Large-Thinking
Open Weight
Mistral Large 3
Proprietary
Trinity-Large-Thinking
Open Weight
Mistral Large 3
2025-12-02
Trinity-Large-Thinking
2026-03-10
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Trinity-Large-Thinking leads the public coding lane, 21 to 18.5, with Supported evidence for both models, although the 90% intervals overlap.
Trinity-Large-Thinking scores higher for agentic tasks on the public lane, 17.7 to 16.9. Mistral Large 3 and Trinity-Large-Thinking are 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.
For the stated presets, chat costs $0.00125 on Mistral Large 3 and $0.0007 on Trinity-Large-Thinking; repository review costs $0.0295 and $0.0152; the cache-heavy agent loop costs $0.125 and $0.064. Mistral Large 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.
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
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Last updated September 29, 2026
