Long documents
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented
MiniMax M3 vs Sage-A1.5
Updated October 20, 2026. Rank says Sage-A1.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
Sage-A1.5 has the higher public point estimate, 62.95 versus 55. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- MiniMax M3 only
- 26
- Sage-A1.5 only
- 10
- Like-for-like categories
- 0 / 8
Supported: MiniMax M3 · Estimated: Sage-A1.5. Conditional ranges do not establish rank confidence.How the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Sage-A1.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Sage-A1.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository 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
Which one for a specific job
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.
36.9MiniMax M354.0Sage-A1.5
Directional only · BenchAlign v5.8
Sage-A1.5 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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
26 MiniMax M3 only
2 Shared
10 Sage-A1.5 only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SWE-bench ProCoding
Normalized gap 2.2MiniMax M3:59%Sage-A1.5:61.2%BrowseCompAgentic
Normalized gap 1.8MiniMax M3:83.5%Sage-A1.5:81.7%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
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.
Agentic
Directional only- MiniMax M3
- 39.2
- Supported · #71/132
- Sage-A1.5
- 57.7
- Estimated · #36/132
- Basis
- BenchAlign v5.8 lane · 10 vs 6 public rows
- Reading
- Directional only
Coding
Directional only- MiniMax M3
- 36.9
- Supported · #75/153
- Sage-A1.5
- 54.0
- Estimated · #34/153
- Basis
- BenchAlign v5.8 lane · 10 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- MiniMax M3
- 93.8
- #6/152
- Sage-A1.5
- 91.9
- #22/152
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- MiniMax M3
- 82.0
- Unranked · 2 rankable rows
- Sage-A1.5
- 80.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M3
- 60.0
- #44/55
- Sage-A1.5
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M3
- 51.7
- Supported · #76/187
- Sage-A1.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M3
- Not ranked
- Sage-A1.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M3
- Not ranked
- Sage-A1.5
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiniMax M3 | Sage-A1.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 39.2Supported · #71/132 | 57.7Estimated · #36/132 | Directional onlyBenchAlign v5.8 lane · 10 vs 6 public rows | Directional only |
| Coding | 36.9Supported · #75/153 | 54.0Estimated · #34/153 | Directional onlyBenchAlign v5.8 lane · 10 vs 3 public rows | Directional only |
| Instruction following | 93.8#6/152 | 91.9#22/152 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 82.0Unranked · 2 rankable rows | 80.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 60.0#44/55 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | 51.7Supported · #76/187 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 1 vs 0 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.
Supported evidence per lane · bars run 0–100Methodology
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
- MiniMax M3
- $0.0009
- Fits in one request
- Sage-A1.5
- Self-hosted; infrastructure cost varies
- Fits in one request
Sage-A1.5 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiniMax M3
- $0.0186
- Fits in one request
- Sage-A1.5
- Self-hosted; infrastructure cost varies
- Fits in one request
Sage-A1.5 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiniMax M3
- $0.03
- Fits in one request
- Sage-A1.5
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Sage-A1.5 has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMiniMax M3Sage-A1.5
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M3
1M
Sage-A1.5
API model ID
MiniMax M3
Not sourced
Sage-A1.5
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiniMax M3
$0.06 per 1M cached input tokens
Sage-A1.5
No comparable hosted API rate
InternScience Sage-A1.5 model cardDocumented inputs
MiniMax M3
Not sourced
Sage-A1.5
Not sourced
Documented outputs
MiniMax M3
Not sourced
Sage-A1.5
Not sourced
Provider availability
MiniMax M3
Not sourced
Sage-A1.5
Not sourced
Reasoning profile
MiniMax M3
Non-Reasoning
Sage-A1.5
Reasoning
Weight access
MiniMax M3
Open Weight
Sage-A1.5
Open Weight
License
MiniMax M3
Open Weight
Sage-A1.5
Open Weight
Release date
MiniMax M3
2026-06-01
Sage-A1.5
2026-10-15
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
- Sage-A1.5 has the higher public point estimate, 62.95 versus 55. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- MiniMax M3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiniMax M3 or Sage-A1.5?
Sage-A1.5 has the higher public point estimate, 62.95 versus 55. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MiniMax M3 or Sage-A1.5?
Sage-A1.5 scores higher for coding on the public lane, 54 to 36.9. Sage-A1.5 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.
Which is better for agentic tasks, MiniMax M3 or Sage-A1.5?
Sage-A1.5 scores higher for agentic tasks on the public lane, 57.7 to 39.2. Sage-A1.5 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.
Which costs less, MiniMax M3 or Sage-A1.5?
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, MiniMax M3 or Sage-A1.5?
MiniMax M3 has the larger documented context window: 1M, compared with 262K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
MiniMax M3
API / mo$1,125
Self-host / mo$13,981
Break-even818M/day
Sage-A1.5
API / mo$0
Self-host / mo$298
Break-even—
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence38 rows
Agentic
Terminal-Bench 2.1
MiniMax M366.0%SourceSage-A1.5—Not directly comparable
BrowseComp
MiniMax M383.5%SourceSage-A1.581.7%SourceMiniMax M3 leads this result
OSWorld-Verified
MiniMax M370.1%SourceSage-A1.5—Not directly comparable
MCP Atlas
MiniMax M374.2%SourceSage-A1.5—Not directly comparable
Claw-Eval
MiniMax M374.5%SourceSage-A1.5—Not directly comparable
BankerToolBench
MiniMax M376.1%SourceSage-A1.5—Not directly comparable
ResearchClawBench
MiniMax M319.8%SourceSage-A1.5—Not directly comparable
OSWorld 2.0
MiniMax M34.6%SourceSage-A1.5—Not directly comparable
Terminal-Bench 2.1 (Vals)
MiniMax M353.6%SourceSage-A1.5—Not directly comparable
MazeBench
MiniMax M30%SourceSage-A1.5—Not directly comparable
GAIA
MiniMax M3—Sage-A1.595.1%SourceNot directly comparable
MLE-Bench Lite
MiniMax M3—Sage-A1.565.2%SourceNot directly comparable
HLE w/ tools
MiniMax M3—Sage-A1.555.1%SourceNot directly comparable
VITA-Bench
MiniMax M3—Sage-A1.545.4%SourceNot directly comparable
PinchBench
MiniMax M3—Sage-A1.590.0%SourceNot directly comparable
Coding
SWE-bench Verified
MiniMax M380.5%SourceSage-A1.5—Not directly comparable
SWE-bench Pro
MiniMax M359%SourceSage-A1.561.2%SourceSage-A1.5 leads this result
Terminal-Bench 2.1
MiniMax M366.0%SourceSage-A1.5—Not directly comparable
NL2Repo
MiniMax M342.1%SourceSage-A1.5—Not directly comparable
VIBE V2
MiniMax M350.1%SourceSage-A1.5—Not directly comparable
SVG-Bench
MiniMax M363.7%SourceSage-A1.5—Not directly comparable
KernelBench Hard
MiniMax M328.8%SourceSage-A1.5—Not directly comparable
OpenHarmony Bench
MiniMax M348.4%SourceSage-A1.5—Not directly comparable
LiveCodeBench (Vals)
MiniMax M382.2%SourceSage-A1.5—Not directly comparable
SWE-bench (Vals)
MiniMax M375.0%SourceSage-A1.5—Not directly comparable
SciCode
MiniMax M3—Sage-A1.544.3%SourceNot directly comparable
SWE Multilingual
MiniMax M3—Sage-A1.572.7%SourceNot directly comparable
Reasoning
CritPt
MiniMax M3—Sage-A1.520.0%SourceNot directly comparable
Multimodal
OfficeQA Pro
MiniMax M345.1%SourceSage-A1.5—Not directly comparable
OmniDocBench 1.5
MiniMax M391.6%SourceSage-A1.5—Not directly comparable
MMMU-Pro
MiniMax M378.1%SourceSage-A1.5—Not directly comparable
VideoMMMU
MiniMax M384.6%SourceSage-A1.5—Not directly comparable
Video-MME (with subtitle)
MiniMax M385.4%SourceSage-A1.5—Not directly comparable
Knowledge
Instruction following
IFBench
MiniMax M3—Sage-A1.581.6%SourceNot directly comparable
Math
USAMO 2026
MiniMax M385.7%SourceSage-A1.5—Not directly comparable
38 public results · 2 shared
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Last updated October 20, 2026