Updated November 13, 2026. Rank says MiniMax M2.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
MiniMax M2.7 has the higher public point estimate, 46.82 versus 43.24. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Arcee AI
43.24/100
Estimated · Public rank #133
Conditional range 28.9–57.6
MiniMax M2.7: $0.30 input / $1.20 output
Trinity-Large-Thinking: $0.25 input / $0.90 output
MiniMax M2.7: 200K
Trinity-Large-Thinking: 512K
MiniMax M2.7: March 18, 2026
Trinity-Large-Thinking: March 10, 2026
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
MiniMax M2.7
MiniMax M2.7 has the higher public coding point estimate, 35.7 to 18.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
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.
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
Trinity-Large-Thinking is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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.
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 v5.8
MiniMax M2.7 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 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 v5.8 lane for agentic, coding, reasoning, multimodal, 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 | MiniMax M2.7 | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 35.7Supported · #83/162 | 18.6Supported · #138/162 | Like-for-likeBenchAlign v5.8 lane · 12 vs 1 public rows | MiniMax M2.7 leads · intervals overlap |
| Agentic | 32.5Supported · #93/135 | 22.7Estimated · #117/135 | Directional onlyBenchAlign v5.8 lane · 8 vs 2 public rows | Directional only |
| Reasoning | 39.2Estimated · #85/160 | 30.2Estimated · #121/160 | Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows | Directional only |
| Knowledge | 46.9Supported · #87/180 | 36.9Estimated · #129/180 | Directional onlyBenchAlign v5.8 lane · 5 vs 2 public rows | Directional only |
| Instruction following | 93.0#14/157 | 28.9#140/157 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 46.6Unranked · 4 rankable rows | 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, reasoning, multimodal, 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.
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
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
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.
MiniMax M2.7
200K
Trinity-Large-Thinking
512K
MiniMax M2.7
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.
MiniMax M2.7
Not published
Trinity-Large-Thinking
Not published
MiniMax M2.7
Trinity-Large-Thinking
MiniMax M2.7
Trinity-Large-Thinking
MiniMax M2.7
Not sourced
Trinity-Large-Thinking
Not sourced
MiniMax M2.7
Non-Reasoning
Trinity-Large-Thinking
Reasoning
MiniMax M2.7
Open Weight
Trinity-Large-Thinking
Open Weight
MiniMax M2.7
Open Weight
Trinity-Large-Thinking
Open Weight
MiniMax M2.7
2026-03-18
Trinity-Large-Thinking
2026-03-10
MiniMax M2.7 has the higher public point estimate, 46.82 versus 43.24. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
MiniMax M2.7 has the higher public coding point estimate, 35.7 to 18.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
MiniMax M2.7 scores higher for agentic tasks on the public lane, 32.5 to 22.7. 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.
For the stated presets, chat costs $0.0009 on MiniMax M2.7 and $0.0007 on Trinity-Large-Thinking; repository review costs $0.0186 and $0.0152; the cache-heavy agent loop costs $0.078 and $0.064. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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 200K.
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.
Terminal-Bench 2.0
Not directly comparable
τ³-bench results
Not directly comparable
Toolathlon
Not directly comparable
MLE-Bench Lite
Not directly comparable
MM-ClawBench
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Shared sourceMiniMax M2.7 leads this result
Terminal-Bench 2.1 (Vals)
Not directly comparable
τ²-bench Airline
Not directly comparable
SWE-bench Verified*
Shared sourceMiniMax M2.7 leads this result
SWE-bench Pro
Not directly comparable
SWE-Rebench
Not directly comparable
SWE Multilingual
Not directly comparable
Multi-SWE Bench
Not directly comparable
VIBE-Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
HLE
Not directly comparable
GPQA-D
MiniMax M2.7 leads this result
MMLU-Pro (Arcee)
Shared sourceTrinity-Large-Thinking leads this result
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
IFBench
Not directly comparable
AIME25 (Arcee)
Shared sourceTrinity-Large-Thinking leads this result
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
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Last updated November 13, 2026
