Updated October 23, 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, 48.09 versus 33.38. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 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.
Tool use, computer use, and multi-step task completion
MiniMax M2.7
MiniMax M2.7 has the higher public agentic point estimate, 32.2 to 12, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Mercury 2.5
Mercury 2.5 has the larger documented context window.
1K fresh input + 500 output tokens
Mercury 2.5
Mercury 2.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mercury 2.5
Mercury 2.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mercury 2.5 is scored on Estimated evidence for coding, 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.
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.
Directional only · 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.
2 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, 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 | Mercury 2.5 | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 12.0Supported · #123/132 | 32.2Supported · #94/132 | Like-for-likeBenchAlign v5.8 lane · 3 vs 8 public rows | MiniMax M2.7 leads |
| Knowledge | 33.8Supported · #139/187 | 45.4Supported · #95/187 | Like-for-likeBenchAlign v5.8 lane · 1 vs 5 public rows | MiniMax M2.7 leads · intervals overlap |
| Coding | 16.1Estimated · #133/153 | 34.4Supported · #82/153 | Directional onlyBenchAlign v5.8 lane · 1 vs 12 public rows | Directional only |
| Instruction following | 81.1#60/153 | 93.0#14/153 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 71.7Unranked · 2 rankable rows | 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 |
| Multilingual | Not ranked | Not ranked | 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
Mercury 2.5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mercury 2.5 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.
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.
Mercury 2.5
MiniMax M2.7
200K
Mercury 2.5
MiniMax M2.7
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mercury 2.5
$0.004 per 1M cached input tokens
Inception models and Mercury 2.5 launch pricingMiniMax M2.7
Not published
Mercury 2.5
Not sourced
MiniMax M2.7
Not sourced
Mercury 2.5
Not sourced
MiniMax M2.7
Not sourced
Mercury 2.5
Not sourced
MiniMax M2.7
Not sourced
Mercury 2.5
Reasoning
MiniMax M2.7
Non-Reasoning
Mercury 2.5
Proprietary
MiniMax M2.7
Open Weight
Mercury 2.5
Proprietary
MiniMax M2.7
Open Weight
Mercury 2.5
2026-09-08
MiniMax M2.7
2026-03-18
MiniMax M2.7 has the higher public point estimate, 48.09 versus 33.38. 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 scores higher for coding on the public lane, 34.4 to 16.1. Mercury 2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
MiniMax M2.7 has the higher public agentic tasks point estimate, 32.2 to 12, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00012 on Mercury 2.5 and $0.0009 on MiniMax M2.7; repository review costs $0.00245 and $0.0186; the cache-heavy agent loop costs $0.0031 and $0.078. 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.
Mercury 2.5 has the larger documented context window: 260K, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
τ³-bench results
Mercury 2.5 leads this result
DeepSearchQA
Not directly comparable
Terminal-Bench 2.1 (Vals)
MiniMax M2.7 leads this result
Terminal-Bench 2.0
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
SciCode
Not directly comparable
SWE-bench Verified*
Not directly comparable
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
GPQA-D
MiniMax M2.7 leads this result
HLE
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
IFBench
Not directly comparable
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Last updated October 23, 2026
