Compare / MiniMax M3.1 Flash vs GPT-6 Luna
MiniMax M3.1 Flash vs GPT-6 Luna
Which is cheaper, which is more capable, and which does better at each kind of work, from 64 results on 4 sources that measured both models.
MiniMaxMiniMax M3.1 Flash
OpenAIGPT-6 Luna
MiniMax M3.1 Flash wins 1011 too close to callGPT-6 Luna wins 43
Of the 64 results both models have, the better value on each. Results whose reported ranges overlap are too close to call.
Results as of 20 October 2026 (catalogue release 2026-10-20-96eb40c3ec63). See also MiniMax vs OpenAI, every model from both labs.
MiniMax M3.1 Flash or GPT-6 Luna? The short answer
Pick MiniMax M3.1 Flash if quality matters most: it scores higher (Spring Prompt overall 47 against 40). Pick GPT-6 Luna if cost matters more: it costs 24% less per token.
- Spring Prompt overall: MiniMax M3.1 Flash 47, GPT-6 Luna 40 (out of 100; how the score works).
- GPT-6 Luna is cheaper: $0.20 against $0.26 per million tokens, 24% less.
- MiniMax M3.1 Flash scores higher on the Artificial Analysis Intelligence Index: 42.4 against 38.1.
- MiniMax M3.1 Flash is newer: released 19 Oct 2026, against 22 Sep 2026 for GPT-6 Luna.
- On our own benchmarks, on the results both models have: GPT-6 Luna does better for product listings, decks from an analysis and marketing planning.
- On third-party benchmarks, on the results both models have: MiniMax M3.1 Flash does better for agents and tool use and reasoning and knowledge.
Head to head
47
Spring Prompt overall
40
42.4
Intelligence Index
38.1
$0.26
Price per million tokens, blendedlower is better
$0.20
159
Output speed, tokens a second
127
1,048,576
Context window, tokens
1,050,000
Every fact side by side
| MiniMax M3.1 Flash | GPT-6 Luna | |
|---|---|---|
| Price per million tokens | $0.15 in · $0.60 out | $0.10 in · $0.50 out |
| Blended (3 in : 1 out) | $0.26 | $0.20 |
| Cheapest host, blended | – | $0.20 (Azure) |
| Cached input, per million | $0.015 cached input | $0.010 cached input |
| Intelligence Index | 42.4 | 38.1 |
| Spring Prompt overall | 47 | 40 |
| Output speed, tokens a second | 159 | 127 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Released | 19 Oct 2026 (newer) | 22 Sep 2026 |
| Weights | Closed | Closed |
| Developer based in | China | the US |
Prices are the developer's list price where we have read it, otherwise the typical host on OpenRouter; see every model's price. Intelligence and speed from Artificial Analysis.
Which is better for what
Each model's rank among the models on sale today, for each kind of work, from our own benchmarks and licensed sources. Further right is better; the better rank is in green.
Product listingsTurning a sparse product feed and photos into listings that can go live
7 of 24
5 of 24
Decks from an analysisTurning a finished analysis into a deck you could present as it is
14 of 24
7 of 24
User surveysPlanning a user survey and reading its results without being misled
13 of 13
not measured
Marketing planningPlanning a year of ad spend without overspending
12 of 24
3 of 24
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
32 of 168
51 of 168
Professional workReal tasks from banking, consulting and law, business documents and freelance projects
not measured
25 of 51
Reasoning and knowledgeHard questions across science, maths and general knowledge
18 of 202
45 of 202
WritingWhat people prefer in blind comparisons, and judged writing quality
not measured
61 of 189
Sticking to the factsSummarising without inventing things, and factual answers
not measured
95 of 153
Speed under pressureGood decisions against a real clock (fast chess)
not measured
20 of 27
From our research
- MiniMax M3.1 Flash first look: careful with product data, unreliable on long jobs 19 Oct 2026
- Best AI for Product Descriptions: Accuracy, Cost and Real Examples 20 Oct 2026
- Mercury 3 first look: very fast with thinking off, too slow with it on 20 Oct 2026
- DeepSeek-V4.1-Flash-1017 review: better at analysis, much worse at sticking to the product data 19 Oct 2026
Every shared result
Each model's best result on every measure both have, grouped by benchmark, with the setting that produced it. Under each measure, every model that benchmark has measured is a grey tick, best to the right, so you can see where the two sit in the field. The headline measures of each benchmark come first; the rest are under "Show all". Green marks the better value; where a source reports ranges that overlap, neither is marked.
| Measure | MiniMax M3.1 Flash | GPT-6 Luna |
|---|---|---|
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 1 · 2 of 24 measured | $0 | $0.0005 |
| Reliably publish-ready% of products, higher is better 9 · 7 of 24 measured | 50.0% | 55.4% |
| Reliably publish-ready · sales brief% of products, higher is better 6 · 4 of 24 measured | 37.5% | 51.8% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 1 · 2 of 24 measured | $0 | $0.0087 |
| Deck ratingrating, higher is betterThe rating weighs each win by how strong the other deck was; the win rate counts wins against whichever decks a model happened to meet, so the two can disagree. 14 · 7 of 24 measured | 1,090 | 1,343 |
| Accurate decks% of tasks, higher is better 9 · 9 of 24 measured | 50.0% | 50.0% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 12 · 3 of 24 measured | 40.3 | 53.4 |
| Audience scorescore out of 100, higher is better 12 · 3 of 24 measured | 60.8 | 68.3 |
| Business scorescore out of 100, higher is better 10 · 3 of 24 measured | 38.9 | 54.8 |
| Artificial Analysis reported by Artificial Analysis · 3 results | ||
| Artificial Analysis Intelligence Indexindex score, higher is better 21 · 39 of 249 measured | 42.4 | 38.1max reasoning |
| Humanity's Last Exam% of questions, higher is better 14 · 51 of 248 measured | 48.0% | 38.5%max reasoning |
| Terminal-Bench 4.0% of tasks, higher is better 22 · 35 of 118 measured | 25.8% | 12.6%max reasoning |
Show all 64 results (52 more)
| Measure | MiniMax M3.1 Flash | GPT-6 Luna |
|---|---|---|
| CatalogBench measured by Spring Prompt · 28 results | ||
| Channel compliance% of products, higher is better 1 · 1 of 24 measured | 100.0% | 100.0% |
| Channel rules broken% of products, lower is better 12 · 1 of 24 measured | 0.6% | 0.0% |
| Channel rules broken · sales brief% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Claims to check% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Claims to check · sales brief% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Conflicts caught% of conflicts, higher is better 1 · 1 of 24 measured | 100.0% | 100.0% |
| Content quality% of checks, higher is better 17 · 11 of 24 measured | 95.8% | 97.0% |
| Content quality · sales brief% of checks, higher is better 17 · 10 of 24 measured | 96.0% | 97.4% |
| Decision accuracy% of decisions, higher is better 16 · 9 of 24 measured | 95.6% | 97.0% |
| Failed outputs% of products, lower is better 22 · 1 of 24 measured | 1.8% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 22 · 1 of 24 measured | 1.8% | 0.0% |
| Field accuracy% of missing fields, higher is better 19 · 10 of 24 measured | 92.0% | 93.7% |
| Invented values% of filled values, lower is better 22 · 13 of 24 measured | 3.8% | 1.7% |
| Missing UK information% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Missing UK information · sales brief% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Not findable% of products, lower is better 16 · 21 of 24 measured | 7.1% | 10.1% |
| Not findable · sales brief% of products, lower is better 12 · 21 of 24 measured | 4.2% | 10.1% |
| Publish-ready listings% of products, higher is better 11 · 8 of 24 measured | 63.1% | 67.9% |
| Publish-ready listings · sales brief% of products, higher is better 7 · 4 of 24 measured | 57.1% | 67.9% |
| Time per productseconds, lower is better 19 · 6 of 24 measured | 32.0 | 8.20 |
| Unsupported claimsclaims per product, lower is better 6 · 5 of 24 measured | 0.39 | 0.31 |
| Unsupported claims% of products, lower is better 5 · 6 of 24 measured | 7.7% | 8.9% |
| Unsupported claims · sales briefclaims per product, lower is better 6 · 4 of 24 measured | 0.60 | 0.35 |
| Unsupported claims · sales brief% of products, lower is better 5 · 4 of 24 measured | 16.1% | 8.3% |
| Wrong attributes% of products, lower is better 20 · 14 of 24 measured | 22.6% | 18.5% |
| Wrong attributes · sales brief% of products, lower is better 20 · 13 of 24 measured | 21.4% | 19.1% |
| Wrong category or variant% of products, lower is better 20 · 5 of 24 measured | 4.2% | 1.8% |
| Wrong category or variant · sales brief% of products, lower is better 21 · 1 of 24 measured | 4.8% | 0.0% |
| DeckBench measured by Spring Prompt · 14 results | ||
| Caveat dropped% of tasks, lower is better 24 · 1 of 24 measured | 33.3% | 0.0% |
| Clean layout% of tasks, higher is better 23 · 6 of 24 measured | 0.0% | 66.7% |
| Design quality% of the maximum, higher is better 21 · 5 of 24 measured | 46.2% | 78.1% |
| Draft figure quoted% of tasks, lower is better 12 · 12 of 24 measured | 33.3% | 33.3% |
| Findings missing% of tasks, lower is better 24 · 1 of 24 measured | 50.0% | 0.0% |
| Head-to-head win rate% of comparisons, higher is betterThe rating weighs each win by how strong the other deck was; the win rate counts wins against whichever decks a model happened to meet, so the two can disagree. 15 · 9 of 24 measured | 48.6% | 75.7% |
| Layout defects% of tasks, lower is better 23 · 6 of 24 measured | 100.0% | 33.3% |
| Misleading metric used% of tasks, lower is better 24 · 1 of 24 measured | 33.3% | 0.0% |
| Presentable decks% of tasks, higher is better 12 · 12 of 24 measured | 0.0% | 0.0% |
| Recommendation late or wrong% of tasks, lower is better 13 · 9 of 24 measured | 33.3% | 16.7% |
| Slides needing work% of tasks, lower is better 21 · 2 of 24 measured | 83.3% | 16.7% |
| Time per deckseconds, lower is better 20 · 14 of 24 measured | 330 | 101 |
| Unsupported claims% of tasks, lower is better 21 · 1 of 24 measured | 50.0% | 0.0% |
| Unsupported numbers% of tasks, lower is better 24 · 1 of 24 measured | 50.0% | 0.0% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 17 · 4 of 24 measured | 17.3 | 35.8 |
| Contribution profitsimulated US dollars, higher is better 5 · 4 of 24 measured | $693,379 | $738,879 |
| Cost of a runUS dollars, lower is better 1 · 2 of 24 measured | $0 | $0.0142 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 24 measured | 0 | 0 |
| Planning scorescore out of 100, higher is better 21 · 12 of 24 measured | 54.2 | 54.8 |
| Return on ad spendprofit per $1 spent, higher is better 9 · 4 of 24 measured | $0.52 | $0.66 |
| Artificial Analysis reported by Artificial Analysis · 4 results | ||
| Long-context reasoning (AA-LCR)% of questions, higher is better 45 · 12 of 239 measured | 79.7% | 83.3%max reasoning |
| Output speedtokens per second, higher is better 21 · 37 of 78 measured | 159 | 127max reasoning |
| SciCode% of problems, higher is better 36 · 29 of 119 measured | 53.4% | 54.6%max reasoning |
| Time to first answer tokenseconds, lower is better 51 · 20 of 78 measured | 13.3 | 0.74no reasoning |
Compare them with other models
- MiniMax vs OpenAIEvery model from both labs, best against best
- MiniMax M3.1 Flash vs Claude Haiku 5.5Cheapest with similar intelligence
- MiniMax M3.1 Flash vs DeepSeek-V4.1-FlashFastest with similar intelligence
- MiniMax M3.1 Flash vs Claude Opus 5.5Most intelligent available today
- GPT-6 Luna vs Claude Haiku 5.5Most intelligent at under $0.25 per million tokens
- GPT-6 Luna vs DeepSeek-V4.1-FlashFastest with similar intelligence
- GPT-6 Luna vs Claude Opus 5.5Most intelligent available today
Quick answers
Is MiniMax M3.1 Flash better than GPT-6 Luna?
Pick MiniMax M3.1 Flash if quality matters most: it scores higher (Spring Prompt overall 47 against 40). Pick GPT-6 Luna if cost matters more: it costs 24% less per token.
Which is cheaper, MiniMax M3.1 Flash or GPT-6 Luna?
GPT-6 Luna is cheaper: $0.20 against $0.26 per million tokens, 24% less.
Which is faster, MiniMax M3.1 Flash or GPT-6 Luna?
MiniMax M3.1 Flash writes about 159 tokens a second on its usual API, against 127 for GPT-6 Luna (Artificial Analysis).
Which is better for product listings, MiniMax M3.1 Flash or GPT-6 Luna?
GPT-6 Luna ranks 5 of 24 models on sale for turning a sparse product feed and photos into listings that can go live, against 7 for MiniMax M3.1 Flash.
Which is better for decks from an analysis, MiniMax M3.1 Flash or GPT-6 Luna?
GPT-6 Luna ranks 7 of 24 models on sale for turning a finished analysis into a deck you could present as it is, against 14 for MiniMax M3.1 Flash.
Which is better for marketing planning, MiniMax M3.1 Flash or GPT-6 Luna?
GPT-6 Luna ranks 3 of 24 models on sale for planning a year of ad spend without overspending, against 12 for MiniMax M3.1 Flash.
Which is better for agents and tool use, MiniMax M3.1 Flash or GPT-6 Luna?
MiniMax M3.1 Flash ranks 32 of 168 models on sale for multi-step tasks with tools: support desks, coding agents, function calls, against 51 for GPT-6 Luna.
Which has the bigger context window, MiniMax M3.1 Flash or GPT-6 Luna?
GPT-6 Luna: 1,050,000 tokens, against 1,048,576 for MiniMax M3.1 Flash.
Run MiniMax M3.1 Flash and GPT-6 Luna on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.