Agentic
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- 41.2
- Estimated · #111/152
- Basis
- BenchAlign lane · 4 vs 7 public rows
- Reading
- Not comparable
LongCat-Flash-Lite-Sparse vs MiniMax M2.7
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
LongCat-Flash-Lite-Sparse
LongCat-Flash-Lite-Sparse has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
LongCat-Flash-Lite-Sparse is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LongCat-Flash-Lite-Sparse is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
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. LongCat-Flash-Lite-Sparse has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
13 LongCat-Flash-Lite-Sparse only
4 Shared
19 MiniMax M2.7 only
- Shared results
- 4
- LongCat-Flash-Lite-Sparse only
- 13
- MiniMax M2.7 only
- 19
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- 48.7
- Estimated · #67/151
- Basis
- BenchAlign lane · 4 vs 11 public rows
- Reading
- Not comparable
Reasoning
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- 74.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- 48.7
- Supported · #90/183
- Basis
- BenchAlign lane · 5 vs 4 public rows
- Reading
- Not comparable
Math
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- MiniMax M2.7
- 93.0
- #10/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | LongCat-Flash-Lite-Sparse | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 41.2Estimated · #111/152 | Not comparableBenchAlign lane · 4 vs 7 public rows | Not comparable |
| Coding | Not ranked | 48.7Estimated · #67/151 | Not comparableBenchAlign lane · 4 vs 11 public rows | Not comparable |
| Reasoning | Not ranked | 74.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 48.7Supported · #90/183 | Not comparableBenchAlign lane · 5 vs 4 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | 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 |
| Instruction following | Not ranked | 93.0#10/123 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
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.
Terminal-Bench 2.0
Agentic
LongCat-Flash-Lite-Sparse: 33.7%MiniMax M2.7: 57%Normalized gap 23.3LongCat-Flash-Lite-Sparse source MiniMax M2.7 sourceSWE Multilingual
Coding
LongCat-Flash-Lite-Sparse: 59.3%MiniMax M2.7: 76.5%Normalized gap 17.2LongCat-Flash-Lite-Sparse source MiniMax M2.7 sourceSWE-bench Pro
Coding
LongCat-Flash-Lite-Sparse: 40.6%MiniMax M2.7: 56.2%Normalized gap 15.6LongCat-Flash-Lite-Sparse source MiniMax M2.7 source
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- LongCat-Flash-Lite-Sparse
- Self-hosted; infrastructure cost varies
- Fits in one request
- MiniMax M2.7
- $0.0009
- Fits in one request
LongCat-Flash-Lite-Sparse has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- LongCat-Flash-Lite-Sparse
- Self-hosted; infrastructure cost varies
- Fits in one request
- MiniMax M2.7
- $0.0186
- Fits in one request
LongCat-Flash-Lite-Sparse has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- LongCat-Flash-Lite-Sparse
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
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. LongCat-Flash-Lite-Sparse has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationLongCat-Flash-Lite-SparseMiniMax M2.7
Context window
Maximum documented context; output-token limits may be lower.
LongCat-Flash-Lite-Sparse
MiniMax M2.7
200K
API model ID
LongCat-Flash-Lite-Sparse
meituan-longcat/LongCat-Flash-Lite-Sparse
Meituan LongCat-Flash-Lite-Sparse model cardMiniMax M2.7
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
LongCat-Flash-Lite-Sparse
No comparable hosted API rate
Meituan LongCat-Flash-Lite-Sparse model cardMiniMax M2.7
Not published
Documented inputs
LongCat-Flash-Lite-Sparse
Not sourced
MiniMax M2.7
Not sourced
Documented outputs
LongCat-Flash-Lite-Sparse
Not sourced
MiniMax M2.7
Not sourced
Provider availability
LongCat-Flash-Lite-Sparse
Not sourced
MiniMax M2.7
Not sourced
Reasoning profile
LongCat-Flash-Lite-Sparse
Reasoning
MiniMax M2.7
Non-Reasoning
Weight access
LongCat-Flash-Lite-Sparse
Open Weight
MiniMax M2.7
Open Weight
License
LongCat-Flash-Lite-Sparse
Open Weight
MiniMax M2.7
Open Weight
Release date
LongCat-Flash-Lite-Sparse
2026-07-31
MiniMax M2.7
2026-03-18
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- LongCat-Flash-Lite-Sparse has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence36 rows
Agentic
Terminal-Bench 2.0
LongCat-Flash-Lite-Sparse33.7%SourceMiniMax M2.757%SourceMiniMax M2.7 leads this result
VITA-Bench
LongCat-Flash-Lite-Sparse21.7%SourceMiniMax M2.7—Not directly comparable
MCP Atlas
LongCat-Flash-Lite-Sparse45.6%SourceMiniMax M2.7—Not directly comparable
BrowseComp
LongCat-Flash-Lite-Sparse48.6%SourceMiniMax M2.7—Not directly comparable
Toolathlon
LongCat-Flash-Lite-Sparse—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
LongCat-Flash-Lite-Sparse—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
LongCat-Flash-Lite-Sparse—MiniMax M2.762.7%SourceNot directly comparable
Claw-Eval
LongCat-Flash-Lite-Sparse—MiniMax M2.748.7%SourceNot directly comparable
Gert Labs
LongCat-Flash-Lite-Sparse—MiniMax M2.740.40%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
LongCat-Flash-Lite-Sparse—MiniMax M2.748.7%SourceNot directly comparable
Coding
SWE-bench Verified
LongCat-Flash-Lite-Sparse68.2%SourceMiniMax M2.7—Not directly comparable
SWE-bench Pro
LongCat-Flash-Lite-Sparse40.6%SourceMiniMax M2.756.2%SourceMiniMax M2.7 leads this result
SWE Multilingual
LongCat-Flash-Lite-Sparse59.3%SourceMiniMax M2.776.5%SourceMiniMax M2.7 leads this result
Terminal-Bench 2.0
LongCat-Flash-Lite-Sparse33.7%SourceMiniMax M2.7—Not directly comparable
SWE-bench Verified*
LongCat-Flash-Lite-Sparse—MiniMax M2.775.4%SourceNot directly comparable
SWE-Rebench
LongCat-Flash-Lite-Sparse—MiniMax M2.751.9%SourceNot directly comparable
Multi-SWE Bench
LongCat-Flash-Lite-Sparse—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
LongCat-Flash-Lite-Sparse—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
LongCat-Flash-Lite-Sparse—MiniMax M2.739.8%SourceNot directly comparable
Vibe Code Bench
LongCat-Flash-Lite-Sparse—MiniMax M2.727.04%SourceNot directly comparable
React Native Evals
LongCat-Flash-Lite-Sparse—MiniMax M2.771.4%SourceNot directly comparable
LiveCodeBench (Vals)
LongCat-Flash-Lite-Sparse—MiniMax M2.779.9%SourceNot directly comparable
SWE-bench (Vals)
LongCat-Flash-Lite-Sparse—MiniMax M2.773.8%SourceNot directly comparable
Knowledge
MMLU
LongCat-Flash-Lite-Sparse85.3%SourceMiniMax M2.7—Not directly comparable
MMLU-Pro
LongCat-Flash-Lite-Sparse79.2%SourceMiniMax M2.7—Not directly comparable
CMMLU
LongCat-Flash-Lite-Sparse84.3%SourceMiniMax M2.7—Not directly comparable
C-Eval
LongCat-Flash-Lite-Sparse85.8%SourceMiniMax M2.7—Not directly comparable
GPQA-D
LongCat-Flash-Lite-Sparse69.5%SourceMiniMax M2.787.0%SourceMiniMax M2.7 leads this result
MMLU-Pro (Arcee)
LongCat-Flash-Lite-Sparse—MiniMax M2.780.8%SourceNot directly comparable
GPQA Diamond (Vals)
LongCat-Flash-Lite-Sparse—MiniMax M2.786.6%SourceNot directly comparable
MMLU-Pro (Vals)
LongCat-Flash-Lite-Sparse—MiniMax M2.780.4%SourceNot directly comparable
Math
MATH-500
LongCat-Flash-Lite-Sparse95.8%SourceMiniMax M2.7—Not directly comparable
AIME26
LongCat-Flash-Lite-Sparse65.7%SourceMiniMax M2.7—Not directly comparable
HMMT Feb 2026
LongCat-Flash-Lite-Sparse40.5%SourceMiniMax M2.7—Not directly comparable
IMOAnswerBench
LongCat-Flash-Lite-Sparse49.4%SourceMiniMax M2.7—Not directly comparable
AIME25 (Arcee)
LongCat-Flash-Lite-Sparse—MiniMax M2.780.0%SourceNot directly comparable
Frequently asked questions
Which is better, LongCat-Flash-Lite-Sparse or MiniMax M2.7?
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.
Which is better for coding, LongCat-Flash-Lite-Sparse or MiniMax M2.7?
LongCat-Flash-Lite-Sparse is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, LongCat-Flash-Lite-Sparse or MiniMax M2.7?
LongCat-Flash-Lite-Sparse is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, LongCat-Flash-Lite-Sparse or MiniMax M2.7?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, LongCat-Flash-Lite-Sparse or MiniMax M2.7?
LongCat-Flash-Lite-Sparse has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated September 10, 2026
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