Long documents
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented
BTL-4 vs MiniMax M3
Updated September 24, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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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. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- BTL-4 only
- 2
- MiniMax M3 only
- 26
- Like-for-like categories
- 0 / 8
Supported: MiniMax M3How 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
BTL-4 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
BTL-4 is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
—BTL-439.2MiniMax M3
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
2 BTL-4 only
1 Shared
26 MiniMax M3 only
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 VerifiedCoding
Normalized gap 2.1BTL-4:78.4%MiniMax M3:80.5%
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 v5.7 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
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- 39.9
- Supported · #45/105
- Basis
- BenchAlign v5.7 lane · 1 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- 39.2
- Supported · #59/135
- Basis
- BenchAlign v5.7 lane · 2 vs 10 public rows
- Reading
- Not comparable
Reasoning
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- 79.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- 52.0
- #36/50
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- 49.3
- Supported · #59/158
- Basis
- BenchAlign v5.7 lane · 0 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- 92.4
- #5/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- BTL-4
- Not ranked
- MiniMax M3
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | BTL-4 | MiniMax M3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 39.9Supported · #45/105 | Not comparableBenchAlign v5.7 lane · 1 vs 9 public rows | Not comparable |
| Coding | Not ranked | 39.2Supported · #59/135 | Not comparableBenchAlign v5.7 lane · 2 vs 10 public rows | Not comparable |
| Reasoning | Not ranked | 79.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 52.0#36/50 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 49.3Supported · #59/158 | Not comparableBenchAlign v5.7 lane · 0 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.4#5/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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
- BTL-4
- Self-hosted; infrastructure cost varies
- Fits in one request
- MiniMax M3
- $0.0009
- Fits in one request
BTL-4 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- BTL-4
- Self-hosted; infrastructure cost varies
- Fits in one request
- MiniMax M3
- $0.0186
- Fits in one request
BTL-4 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- BTL-4
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- MiniMax M3
- $0.03
- Fits in one request
BTL-4 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.
SpecificationBTL-4MiniMax M3
Context window
Maximum documented context; output-token limits may be lower.
BTL-4
MiniMax M3
1M
API model ID
BTL-4
Not sourced
MiniMax M3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
BTL-4
No comparable hosted API rate
Bad Theory Labs BTL-4 model cardMiniMax M3
$0.06 per 1M cached input tokens
Documented inputs
BTL-4
Not sourced
MiniMax M3
Not sourced
Documented outputs
BTL-4
Not sourced
MiniMax M3
Not sourced
Provider availability
BTL-4
Not sourced
MiniMax M3
Not sourced
Reasoning profile
BTL-4
Reasoning
MiniMax M3
Non-Reasoning
Weight access
BTL-4
Open Weight
MiniMax M3
Open Weight
License
BTL-4
Open Weight
MiniMax M3
Open Weight
Release date
BTL-4
2026-08-05
MiniMax M3
2026-06-01
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
- MiniMax M3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, BTL-4 or MiniMax M3?
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, BTL-4 or MiniMax M3?
BTL-4 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, BTL-4 or MiniMax M3?
BTL-4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, BTL-4 or MiniMax M3?
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, BTL-4 or MiniMax M3?
MiniMax M3 has the larger documented context window: 1M, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
BFCL v4
BTL-473.5%SourceMiniMax M3—Not directly comparable
Terminal-Bench 2.1
BTL-4—MiniMax M366.0%SourceNot directly comparable
BrowseComp
BTL-4—MiniMax M383.5%SourceNot directly comparable
OSWorld-Verified
BTL-4—MiniMax M370.1%SourceNot directly comparable
MCP Atlas
BTL-4—MiniMax M374.2%SourceNot directly comparable
Claw-Eval
BTL-4—MiniMax M374.5%SourceNot directly comparable
BankerToolBench
BTL-4—MiniMax M376.1%SourceNot directly comparable
ResearchClawBench
BTL-4—MiniMax M319.8%SourceNot directly comparable
OSWorld 2.0
BTL-4—MiniMax M34.6%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
BTL-4—MiniMax M353.6%SourceNot directly comparable
Coding
SWE-bench Verified
BTL-478.4%SourceMiniMax M380.5%SourceMiniMax M3 leads this result
LiveCodeBench v6
BTL-466.1%SourceMiniMax M3—Not directly comparable
SWE-bench Pro
BTL-4—MiniMax M359%SourceNot directly comparable
Terminal-Bench 2.1
BTL-4—MiniMax M366.0%SourceNot directly comparable
NL2Repo
BTL-4—MiniMax M342.1%SourceNot directly comparable
VIBE V2
BTL-4—MiniMax M350.1%SourceNot directly comparable
SVG-Bench
BTL-4—MiniMax M363.7%SourceNot directly comparable
KernelBench Hard
BTL-4—MiniMax M328.8%SourceNot directly comparable
OpenHarmony Bench
BTL-4—MiniMax M348.4%SourceNot directly comparable
LiveCodeBench (Vals)
BTL-4—MiniMax M382.2%SourceNot directly comparable
SWE-bench (Vals)
BTL-4—MiniMax M375.0%SourceNot directly comparable
Multimodal
OfficeQA Pro
BTL-4—MiniMax M345.1%SourceNot directly comparable
OmniDocBench 1.5
BTL-4—MiniMax M391.6%SourceNot directly comparable
MMMU-Pro
BTL-4—MiniMax M378.1%SourceNot directly comparable
VideoMMMU
BTL-4—MiniMax M384.6%SourceNot directly comparable
Video-MME (with subtitle)
BTL-4—MiniMax M385.4%SourceNot directly comparable
Knowledge
Math
USAMO 2026
BTL-4—MiniMax M385.7%SourceNot directly comparable
29 public results · 1 shared
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Last updated September 24, 2026