Updated October 19, 2026. Rank says MiniMax M3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
MiniMax M3 has the higher public score estimate, 54.97 versus 49.61, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 9 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
MiniMax M3 has the higher public coding point estimate, 37 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
MiniMax M3
MiniMax M3 has the higher public agentic point estimate, 39.3 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
MiniMax M3
MiniMax M3 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
MiniMax M3 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
MiniMax M3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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 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.
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 4.8OSWorld-VerifiedAgentic
Normalized gap 3.9LiveCodeBench (Vals)Coding
Normalized gap 3.2MMLU-Pro (Vals)Knowledge
Normalized gap 1.6Each 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 | Gemini 3.5 Flash-Lite | MiniMax M3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 35.6Supported · #81/130 | 39.3Supported · #70/130 | Like-for-likeBenchAlign v5.8 lane · 3 vs 10 public rows | MiniMax M3 leads · intervals overlap |
| Coding | 35.8Supported · #77/151 | 37.0Supported · #74/151 | Like-for-likeBenchAlign v5.8 lane · 4 vs 10 public rows | MiniMax M3 leads · intervals overlap |
| Knowledge | 49.1Supported · #86/186 | 51.6Supported · #76/186 | Like-for-likeBenchAlign v5.8 lane · 2 vs 2 public rows | MiniMax M3 leads · intervals overlap |
| Reasoning | 70.9Unranked · 3 rankable rows | 82.0Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 80.9Unranked · 1 rankable row | 60.0#44/55 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.8#5/148 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 37.8Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M3 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.
Gemini 3.5 Flash-Lite
MiniMax M3
1M
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationMiniMax M3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingMiniMax M3
$0.06 per 1M cached input tokens
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationMiniMax M3
Not sourced
Gemini 3.5 Flash-Lite
MiniMax M3
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMiniMax M3
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
MiniMax M3
Non-Reasoning
Gemini 3.5 Flash-Lite
Proprietary
MiniMax M3
Open Weight
Gemini 3.5 Flash-Lite
Proprietary
MiniMax M3
Open Weight
Gemini 3.5 Flash-Lite
2026-07-21
MiniMax M3
2026-06-01
MiniMax M3 has the higher public score estimate, 54.97 versus 49.61, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
MiniMax M3 has the higher public coding point estimate, 37 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
MiniMax M3 has the higher public agentic tasks point estimate, 39.3 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.0009 on MiniMax M3; repository review costs $0.0225 and $0.0186; the cache-heavy agent loop costs $0.037 and $0.03. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
MiniMax M3 leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite leads this result
Terminal-Bench 2.1 (Vals)
MiniMax M3 leads this result
BrowseComp
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Not directly comparable
BankerToolBench
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
MazeBench
Not directly comparable
Terminal-Bench 2.1
MiniMax M3 leads this result
SWE-bench Pro
MiniMax M3 leads this result
LiveCodeBench (Vals)
MiniMax M3 leads this result
SWE-bench (Vals)
Tie
SWE-bench Verified
Not directly comparable
NL2Repo
Not directly comparable
VIBE V2
Not directly comparable
SVG-Bench
Not directly comparable
KernelBench Hard
Not directly comparable
OpenHarmony Bench
Not directly comparable
MRCRv2
Not directly comparable
OfficeQA Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
MMMU-Pro
Not directly comparable
VideoMMMU
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 5,600+ readers.
Last updated October 19, 2026
