Chat turn cost
1K fresh input + 500 output tokens
d1-ultra
d1-ultra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
d1-ultra vs MiniMax M3.1 Flash
Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- d1-ultra only
- 0
- MiniMax M3.1 Flash only
- 12
- Like-for-like categories
- 0 / 8
Estimated: MiniMax M3.1 Flash. 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
d1-ultra
d1-ultra has the lower estimated token cost for this stated workload. d1-ultra has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
d1-ultra
d1-ultra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
D1-ultra 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
D1-ultra is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limited
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.
—d1-ultra61.3MiniMax M3.1 Flash
Not comparable · BenchAlign v5.8
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.
12 MiniMax M3.1 Flash 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- 63.9
- Estimated · #19/130
- Basis
- BenchAlign v5.8 lane · 0 vs 6 public rows
- Reading
- Not comparable
Coding
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- 61.3
- Supported · #17/151
- Basis
- BenchAlign v5.8 lane · 0 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- 80.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- 73.5
- #30/55
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- 73.2
- Supported · #14/186
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- d1-ultra
- Not ranked
- MiniMax M3.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | d1-ultra | MiniMax M3.1 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 63.9Estimated · #19/130 | Not comparableBenchAlign v5.8 lane · 0 vs 6 public rows | Not comparable |
| Coding | Not ranked | 61.3Supported · #17/151 | Not comparableBenchAlign v5.8 lane · 0 vs 3 public rows | Not comparable |
| Reasoning | Not ranked | 80.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 73.5#30/55 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | Not ranked | 73.2Supported · #14/186 | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | 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 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
- d1-ultra
- $0.00004
- Fit state unavailable
- MiniMax M3.1 Flash
- $0.00045
- Fits in one request
d1-ultra has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- d1-ultra
- $0.0021
- Fit state unavailable
- MiniMax M3.1 Flash
- $0.0093
- Fits in one request
d1-ultra has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- d1-ultra
- $0.00924
- Fit state unavailable
- Cached input priced at the published list-input rate
- MiniMax M3.1 Flash
- $0.012
- Fits in one request
d1-ultra has the lower modeled cost
d1-ultra has no published cached-input rate, so cached tokens use its listed input 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.
Specificationd1-ultraMiniMax M3.1 Flash
Context window
Maximum documented context; output-token limits may be lower.
d1-ultra
Not sourced
Liquid AI d1-ultra launchMiniMax M3.1 Flash
API model ID
d1-ultra
d1-ultra
Liquid AI d1-ultra launchMiniMax M3.1 Flash
MiniMax-M3.1-Flash
MiniMax: M3.1 Flash launch postCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
d1-ultra
Not published
Liquid AI d1-ultra launch postMiniMax M3.1 Flash
$0.015 per 1M cached input tokens
MiniMax: M3.1 Flash launch postDocumented inputs
d1-ultra
text, image
Liquid AI d1-ultra launchMiniMax M3.1 Flash
Not sourced
Documented outputs
d1-ultra
typed JSON
Liquid AI d1-ultra launchMiniMax M3.1 Flash
Not sourced
Provider availability
d1-ultra
Generally Available · Liquid API, Liquid Console
Liquid AI d1-ultra launchMiniMax M3.1 Flash
Not sourced
Reasoning profile
d1-ultra
Non-Reasoning
MiniMax M3.1 Flash
Reasoning
Weight access
d1-ultra
Proprietary
MiniMax M3.1 Flash
Proprietary
License
d1-ultra
Proprietary
MiniMax M3.1 Flash
Proprietary
Release date
d1-ultra
2026-10-15
MiniMax M3.1 Flash
2026-10-19
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0021 vs $0.0093. Cache-heavy agent loop: $0.00924 vs $0.012.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, d1-ultra or MiniMax M3.1 Flash?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, d1-ultra or MiniMax M3.1 Flash?
D1-ultra is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, d1-ultra or MiniMax M3.1 Flash?
D1-ultra is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, d1-ultra or MiniMax M3.1 Flash?
For the stated presets, chat costs $0.00004 on d1-ultra and $0.00045 on MiniMax M3.1 Flash; repository review costs $0.0021 and $0.0093; the cache-heavy agent loop costs $0.00924 and $0.012. d1-ultra has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, d1-ultra or MiniMax M3.1 Flash?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence12 rows
Agentic
AutomationBench
d1-ultra—MiniMax M3.1 Flash62.2%SourceNot directly comparable
BankerToolBench
d1-ultra—MiniMax M3.1 Flash76.2%SourceNot directly comparable
Terminal-Bench 4.0
d1-ultra—MiniMax M3.1 Flash38.10%SourceNot directly comparable
Agents' Last Exam
d1-ultra—MiniMax M3.1 Flash29.6%SourceNot directly comparable
OSWorld 2.0
d1-ultra—MiniMax M3.1 Flash61.7%SourceNot directly comparable
CyberGym
d1-ultra—MiniMax M3.1 Flash82.2%SourceNot directly comparable
Coding
Multimodal
Knowledge
HLE w/o tools
d1-ultra—MiniMax M3.1 Flash43.4%SourceNot directly comparable
12 public results · 0 shared
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Last updated October 19, 2026