MiniMax M3 vs GPT-5.6 Luna
At a Glance
| Compare |  MiniMax M3MiniMax |  GPT-5.6 LunaOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #32 of 5733.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 22.6–55.9 | UnrankedNot in the 57-model eligible cohort |
| CostLower is better · Published-token output estimate | #3 of 27$0.017 per LiveBench case | UnrankedNot in the 27-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.9 | #15 of 2749.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 43.4–60.0 | UnrankedNot in the 27-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.28Deepinfra ↗ · Oct 19, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $1.10Deepinfra ↗ · Oct 19, 2026 | $1.20Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,050K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | MiniMax-M3 | GPT-5.6 Luna |
|---|---|---|
| LMArena Document Arenadocument-2026-09-13-a64ca3b12b6e · arena_rating · leader | 1,434.7998% of row best · rating · minimax-m3; 95% CI [1426.53255805, 1443.05504941]; votes 6314; rank 32 | 1,456.92100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1448.72029252, 1465.11301394]; votes 5766; rank 23 |
| LMArena Text Arenatext-2026-10-15-a64ca3b12b6e · arena_rating · statistical tie | 1,433.33100% of row best · rating · minimax-m3; 95% CI [1429.51159020, 1437.14164071]; votes 63973; rank 91 | 1,430.66100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1426.38028671, 1434.93119450]; votes 41109; rank 93 |
| LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e · arena_rating · statistical tie | 1,253.11100% of row best · rating · minimax-m3; 95% CI [1246.68252453, 1259.53765792]; votes 19026; rank 60 | 1,258.24100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1250.82216689, 1265.66350676]; votes 11086; rank 58 |
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 70.2691% of row best · percent · minimax-m3 · 15,530 output tokens / case | 77.05100% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 74.7995% of row best · points · MiniMax M3 (thinking on) · 31,777 output tokens / case | 78.83100% of row best · points · GPT-5.6 Luna (ultra) · 19,621 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 2 ties | 0 benchmark wins | 2 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.
Side-by-Side Facts
| Field | MiniMax-M3 | GPT-5.6 Luna |
|---|---|---|
| Developer | MiniMax | OpenAI |
| Family | Minimax M3 | Gpt 5 6 |
| Model | MiniMax-M3 | GPT-5.6 Luna |
| Version | MiniMax-M3 | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | 2026-06-01 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,050K |
| Total parameters | 427B | Unknown |
| Active parameters | 23B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
MiniMax M3 Capabilities
GPT-5.6 Luna Capabilities
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Primary Evidence
Sources and Freshness
Questions
MiniMax M3 vs GPT-5.6 Luna FAQs
Is MiniMax M3 or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M3 and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, MiniMax M3 or GPT-5.6 Luna?+
MiniMax M3 is $0.28 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. MiniMax M3 is $1.10 and GPT-5.6 Luna is $1.20 per million tokens, so MiniMax M3 is cheaper on this metric.
Which has a larger context window, MiniMax M3 or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. MiniMax M3 supports 1,048,576 tokens and GPT-5.6 Luna supports 1,050,000 tokens.
Which performs better in benchmarks, MiniMax M3 or GPT-5.6 Luna?+
GPT-5.6 Luna leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can MiniMax M3 or GPT-5.6 Luna be self-hosted?+
MiniMax M3 is documented as self-hostable; GPT-5.6 Luna is documented as not self-hostable. MiniMax M3 is open weight; GPT-5.6 Luna is not marked open weight.
Can MiniMax M3 and GPT-5.6 Luna understand images?+
MiniMax M3 is documented with image input; GPT-5.6 Luna is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, MiniMax M3 or GPT-5.6 Luna?+
GPT-5.6 Luna has a sourced maximum output of 128,000 tokens; MiniMax M3's maximum output is not reported. A larger limit cannot be determined.
Do MiniMax M3 and GPT-5.6 Luna support reasoning and tool use?+
MiniMax M3: reasoning, tool calling, and image input. GPT-5.6 Luna: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, MiniMax M3 or GPT-5.6 Luna?+
MiniMax M3 has 5 sourced provider routes; GPT-5.6 Luna has 2, so MiniMax M3 has broader tracked availability.
Which offers better value, MiniMax M3 or GPT-5.6 Luna?+
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.