Updated October 20, 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, 48.15 versus 45.07. Their conditional score ranges overlap. These ranges do not establish rank confidence. 11 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, 34.4 to 31.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Ling 3.0 Flash
Ling 3.0 Flash has the higher public agentic point estimate, 33.4 to 32.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Ling 3.0 Flash
Ling 3.0 Flash has the larger documented context window.
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. Ling 3.0 Flash 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.
1 category rests 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.
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.
HMMT Feb 2026Math
Normalized gap 14.3HLEKnowledge
Normalized gap 5.3SWE MultilingualCoding
Normalized gap 4.1LiveCodeBench (Vals)Coding
Normalized gap 4.1AIME26Math
Normalized gap 3.4Each 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 | Ling 3.0 Flash | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 33.4Supported · #87/131 | 32.3Supported · #93/131 | Like-for-likeBenchAlign v5.8 lane · 9 vs 8 public rows | Ling 3.0 Flash leads · intervals overlap |
| Coding | 31.6Supported · #92/152 | 34.4Supported · #81/152 | Like-for-likeBenchAlign v5.8 lane · 6 vs 12 public rows | MiniMax M2.7 leads · intervals overlap |
| Knowledge | 43.5Supported · #107/187 | 46.0Supported · #95/187 | Like-for-likeBenchAlign v5.8 lane · 5 vs 5 public rows | MiniMax M2.7 leads · intervals overlap |
| Instruction following | 75.2#68/149 | 93.0#13/149 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 75.1Unranked · 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 | 81.1Unranked · 3 rankable rows | 70.5Unranked · 4 rankable rows | Not comparableProvisional lane · 2 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
Ling 3.0 Flash has no comparable published API token rate.
50K fresh input + 3K output tokens
Ling 3.0 Flash 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. Ling 3.0 Flash 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.
Ling 3.0 Flash
MiniMax M2.7
200K
Ling 3.0 Flash
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.
Ling 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardMiniMax M2.7
Not published
Ling 3.0 Flash
Not sourced
MiniMax M2.7
Not sourced
Ling 3.0 Flash
Not sourced
MiniMax M2.7
Not sourced
Ling 3.0 Flash
Not sourced
MiniMax M2.7
Not sourced
Ling 3.0 Flash
Reasoning
MiniMax M2.7
Non-Reasoning
Ling 3.0 Flash
Open Weight
MiniMax M2.7
Open Weight
Ling 3.0 Flash
Open Weight
MiniMax M2.7
Open Weight
Ling 3.0 Flash
2026-07-23
MiniMax M2.7
2026-03-18
MiniMax M2.7 has the higher public point estimate, 48.15 versus 45.07. 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, 34.4 to 31.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Ling 3.0 Flash has the higher public agentic tasks point estimate, 33.4 to 32.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Ling 3.0 Flash has the larger documented context window: 262K, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
MCP Atlas
Not directly comparable
skillsBench
Not directly comparable
BFCL v4
Not directly comparable
WideResearch
Not directly comparable
BrowseComp
Not directly comparable
DRACO
Not directly comparable
Terminal-Bench 2.1 (Vals)
Ling 3.0 Flash leads this result
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
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
Not directly comparable
SWE-bench Pro
Ling 3.0 Flash leads this result
SWE Multilingual
MiniMax M2.7 leads this result
LiveCodeBench v5
Not directly comparable
SciCode
Not directly comparable
LiveCodeBench (Vals)
Ling 3.0 Flash 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
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
GPQA
Not directly comparable
GPQA-D
MiniMax M2.7 leads this result
HLE
MiniMax M2.7 leads this result
GPQA Diamond (Vals)
MiniMax M2.7 leads this result
MMLU-Pro (Vals)
Ling 3.0 Flash leads this result
MMLU-Pro (Arcee)
Not directly comparable
IFBench
Not directly comparable
AIME26
Ling 3.0 Flash leads this result
HMMT Feb 2026
Ling 3.0 Flash leads this result
IMOAnswerBench
Not directly comparable
AIME25 (Arcee)
Not directly comparable
HMMT Nov 2025
Not directly comparable
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Last updated October 20, 2026
