Updated October 25, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 9 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
MiniMax M2.7
MiniMax M2.7 has the higher public coding point estimate, 34.7 to 25.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Laguna M.1
Laguna M.1 has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna M.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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. Laguna M.1 has no comparable published API token rate.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
SWE MultilingualCoding
Normalized gap 13.4LiveCodeBench (Vals)Coding
Normalized gap 11.8MMLU-Pro (Vals)Knowledge
Normalized gap 11.6Terminal-Bench 2.0Agentic
Normalized gap 11.2SWE-bench ProCoding
Normalized gap 7.0Each 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.
| Category | Laguna M.1 | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Coding | 25.9Supported · #109/153 | 34.7Supported · #82/153 | Like-for-likeBenchAlign v5.8 lane · 6 vs 12 public rows | MiniMax M2.7 leads · intervals overlap |
| Agentic | Not ranked | 32.5Supported · #92/132 | Not comparableBenchAlign v5.8 lane · 2 vs 8 public rows | Not comparable |
| Reasoning | Not ranked | 78.8Unranked · 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 |
| Knowledge | Not ranked | 45.8Supported · #95/187 | Not comparableBenchAlign v5.8 lane · 2 vs 5 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.0#14/153 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 70.5Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 2 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.
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
Laguna M.1 has no comparable published API token rate.
50K fresh input + 3K output tokens
Laguna M.1 has no comparable published API token rate.
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. Laguna M.1 has no comparable published API token 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.
Laguna M.1
256K
MiniMax M2.7
200K
Laguna M.1
Not sourced
MiniMax M2.7
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Laguna M.1
No comparable hosted API rate
MiniMax M2.7
Not published
Laguna M.1
Not sourced
MiniMax M2.7
Not sourced
Laguna M.1
Not sourced
MiniMax M2.7
Not sourced
Laguna M.1
Not sourced
MiniMax M2.7
Not sourced
Laguna M.1
Reasoning
MiniMax M2.7
Non-Reasoning
Laguna M.1
Proprietary
MiniMax M2.7
Open Weight
Laguna M.1
Proprietary
MiniMax M2.7
Open Weight
Laguna M.1
2026-04-28
MiniMax M2.7
2026-03-18
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
MiniMax M2.7 has the higher public coding point estimate, 34.7 to 25.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Laguna M.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Laguna M.1 has the larger documented context window: 256K, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
MiniMax M2.7 leads this result
Terminal-Bench 2.1 (Vals)
MiniMax M2.7 leads this result
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
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
MiniMax M2.7 leads this result
SWE-bench Pro
MiniMax M2.7 leads this result
Terminal-Bench 2.0
MiniMax M2.7 leads this result
LiveCodeBench (Vals)
MiniMax M2.7 leads this result
SWE-bench (Vals)
MiniMax M2.7 leads this result
SWE-bench Verified*
Not directly comparable
SWE-Rebench
Not directly comparable
Multi-SWE Bench
Not directly comparable
VIBE-Pro
Not directly comparable
NL2Repo
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
GPQA Diamond (Vals)
MiniMax M2.7 leads this result
MMLU-Pro (Vals)
MiniMax M2.7 leads this result
HLE
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
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Last updated October 25, 2026
