MiniMax M3 vs Qwen3.8 27B

Market Position

LiveBench Quality Versus Estimated Output Cost

Full ranking →
Efficiency FrontierLiveBench overall · output estimate
Upper-left is better
xAIDeepSeekZ.aiMiniMaxOpenAIQwenGoogle DeepMindMoonshot AIAnthropic
LiveBench quality versus score-adjusted output costEach dot is a reviewed major-model configuration and is colored by developer. Higher means a better LiveBench overall score. Farther left means lower estimated output cost after adjusting by the score. A dotted line connects the non-dominated frontier observations. When models are selected, their sourced families remain prominent, unrelated observations retain their developer colors at lower opacity, and an orange ring identifies each selected model.$0.0050$0.010$0.020$0.050$0.100$0.200$0.5006873788287Grok Build 0.1GLM 5.3 FlashGPT-5.6 Luna (max)DeepSeek V4 Flash Vision ExpGPT-5.6 Sol (max)Score-adjusted output cost per LiveBench case (log) →LiveBench overall →
The dotted frontier connects measured, non-dominated major-model observations. With a selection, sourced families stay prominent, unrelated observations retain their developer colors at lower opacity, and orange rings mark the selected model or models. Family lines connect models only when their sourced family and generation match. Cost is estimated from published output tokens and the lowest current USD output rate; it excludes input, caching, batch discounts, and provider-specific benchmark execution details.

This current-market view appears only when both compared models are reviewed current models with publisher-reported LiveBench token accounting and current sourced USD output rates.

Model Markets Rankings

Intelligence, Cost, and Efficiency

All rankings →
RankingMiniMax-M3Qwen3.8-27B
CostLower is better · Published-token output estimate#4 of 36$0.017 per LiveBench case#16 of 36$0.086 per LiveBench case

Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.

Benchmark Performance

Available Benchmarks

BenchmarkMiniMax-M3Qwen3.8-27B
LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · statistical tie1,435.20100% of row best · rating · minimax-m3; 95% CI [1430.86827835, 1439.53977389]; votes 44544; rank 771,437.35100% of row best · rating · qwen3.8-27b; 95% CI [1429.71618499, 1444.98971204]; votes 6626; rank 69
LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · leader1,255.4298% of row best · rating · minimax-m3; 95% CI [1247.93588333, 1262.90862002]; votes 13390; rank 441,279.32100% of row best · rating · qwen3.8-27b; 95% CI [1267.46655532, 1291.16933290]; votes 2954; rank 28
LiveBench2026-06-25 · overall · leader70.2690% of row best · percent · minimax-m3 · 15,530 output tokens / case78.02100% of row best · percent · qwen3.8-27b · 28,740 output tokens / case
Overall ResultCounted from the protocol-matched rows above · 1 tie0 benchmark wins2 benchmark winsOverall lead

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

FieldAt a Glance
MiniMax · activeMiniMax M3Verified Aug 28, 2026
Qwen · activeQwen3.8 27BVerified Aug 28, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldMiniMax-M3Qwen3.8-27B
DeveloperMiniMaxQwen
FamilyMinimax M3Qwen3 8 27b
ModelMiniMax-M3Qwen3.8-27B
VersionMiniMax-M3Qwen3.8-27B
Lifecycleactiveactive
Released2026-06-01Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,049K262K
Total parameters427B27.8B
Active parameters23BUnknown
Licenseotherapache-2.0
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessDeepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, reasoning, tools

15 comparable fields · 8 material differences · Pair passes the primary-source comparison gate

MiniMax M3 Capabilities

chatgenerationreasoningtools
Input price$0.28
Output price$1.10
Serving providers5
Canonical IDMiniMaxAI/MiniMax-M3

Qwen3.8 27B Capabilities

chatgenerationreasoningtools
Input price$0.40
Output price$3.00
Serving providers3
Canonical IDQwen/Qwen3.8-27B

Internal Comparison Graph

Related Comparisons

All image comparisons →
APairBContext
vsfamily variantstext
vscross-developer peersimage, text
vscross-developer peersimage, text
vscross-developer peerstext
vscross-developer peerstext
vscross-developer peersimage, text
vscross-developer peersimage, text
vscross-developer peersimage, text
vscross-developer peerstext
vscross-developer peerstext
vscross-developer peersimage, text
vscross-developer peersimage, text

Primary Evidence

Sources and Freshness

Questions

MiniMax M3 vs Qwen3.8 27B FAQs

Is MiniMax M3 or Qwen3.8 27B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M3 and Qwen3.8 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, MiniMax M3 or Qwen3.8 27B?+

MiniMax M3 is $0.28 and Qwen3.8 27B is $0.40 per million tokens, so MiniMax M3 is cheaper on this metric. MiniMax M3 is $1.10 and Qwen3.8 27B is $3.00 per million tokens, so MiniMax M3 is cheaper on this metric.

Which has a larger context window, MiniMax M3 or Qwen3.8 27B?+

MiniMax M3 has the larger sourced context window. MiniMax M3 supports 1,049K and Qwen3.8 27B supports 262K.

Which performs better in benchmarks, MiniMax M3 or Qwen3.8 27B?+

Qwen3.8 27B leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.

Can MiniMax M3 or Qwen3.8 27B be self-hosted?+

Both models have the same recorded self-hosting status: supported. MiniMax M3 is open weight; Qwen3.8 27B is open weight.

Can MiniMax M3 and Qwen3.8 27B understand images?+

MiniMax M3 is documented with image input; Qwen3.8 27B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, MiniMax M3 or Qwen3.8 27B?+

Neither has a larger sourced maximum output. MiniMax M3 is — and Qwen3.8 27B is 131K.

Do MiniMax M3 and Qwen3.8 27B support reasoning and tool use?+

MiniMax M3: reasoning, tool calling, and image input. Qwen3.8 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, MiniMax M3 or Qwen3.8 27B?+

MiniMax M3 has 5 sourced provider routes; Qwen3.8 27B has 3, so MiniMax M3 has broader tracked availability.

Which offers better value, MiniMax M3 or Qwen3.8 27B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

Send Feedback