Updated October 19, 2026. Rank says MiniMax M3.1 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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.
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 M3.1 Flash
MiniMax M3.1 Flash has the higher public coding point estimate, 61.3 to 45.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiniMax M3.1 Flash and Mistral Large 4 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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 M3.1 Flash 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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.
| Category | MiniMax M3.1 Flash | Mistral Large 4 | Basis | Reading |
|---|---|---|---|---|
| Coding | 61.3Supported · #17/151 | 45.6Supported · #54/151 | Like-for-likeBenchAlign v5.8 lane · 3 vs 1 public rows | MiniMax M3.1 Flash leads · intervals overlap |
| Knowledge | 73.2Supported · #14/186 | 54.6Supported · #64/186 | Like-for-likeBenchAlign v5.8 lane · 1 vs 0 public rows | MiniMax M3.1 Flash leads · intervals overlap |
| Agentic | 63.9Estimated · #19/130 | 50.1Estimated · #48/130 | Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows | Directional only |
| Reasoning | 80.6Unranked · 2 rankable rows | 81.7#8/26 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 73.5#30/55 | 77.0Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted 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.
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
MiniMax M3.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M3.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M3.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
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.
MiniMax M3.1 Flash
Mistral Large 4
MiniMax M3.1 Flash
MiniMax-M3.1-Flash
MiniMax: M3.1 Flash launch postMistral Large 4
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiniMax M3.1 Flash
$0.015 per 1M cached input tokens
MiniMax: M3.1 Flash launch postMistral Large 4
$0.07 per 1M cached input tokens
Mistral AI Mistral Large 4 model documentationMiniMax M3.1 Flash
Not sourced
Mistral Large 4
Not sourced
MiniMax M3.1 Flash
Not sourced
Mistral Large 4
Not sourced
MiniMax M3.1 Flash
Not sourced
Mistral Large 4
Not sourced
MiniMax M3.1 Flash
Reasoning
Mistral Large 4
Hybrid
MiniMax M3.1 Flash
Proprietary
Mistral Large 4
Pending
MiniMax M3.1 Flash
Proprietary
Mistral Large 4
Pending
MiniMax M3.1 Flash
2026-10-19
Mistral Large 4
2026-10-06
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.
MiniMax M3.1 Flash has the higher public coding point estimate, 61.3 to 45.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
MiniMax M3.1 Flash scores higher for agentic tasks on the public lane, 63.9 to 50.1. MiniMax M3.1 Flash and Mistral Large 4 are 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.
For the stated presets, chat costs $0.00045 on MiniMax M3.1 Flash and $0.00172 on Mistral Large 4; repository review costs $0.0093 and $0.04027; the cache-heavy agent loop costs $0.012 and $0.0485. Costs use the listed standard API rates.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
AutomationBench
Not directly comparable
BankerToolBench
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
Agents' Last Exam
Not directly comparable
OSWorld 2.0
Not directly comparable
CyberGym
Not directly comparable
Cybench
Not directly comparable
Finance Agent v2
Not directly comparable
PostTrainBench v1.1
Not directly comparable
DeepSWE
Not directly comparable
NL2Repo
Not directly comparable
Vibe Code Bench
Not directly comparable
STEMBench
Not directly comparable
HLE w/o tools
Not directly comparable
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Last updated October 19, 2026
