Compare / Mercury 3 vs GPT-6 Luna
Mercury 3 vs GPT-6 Luna
Which is cheaper, which is more capable, and which does better at each kind of work, from 31 results on 3 sources that measured both models.
Of the 31 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).
Mercury 3 or GPT-6 Luna? The short answer
They score about the same, so pick GPT-6 Luna: it costs 26% less per deck on DeckBench.
- Spring Prompt overall: Mercury 3 39, GPT-6 Luna 40 (out of 100; how the score works).
- GPT-6 Luna is cheaper to run: $0.009 against $0.012 per deck on DeckBench (26% less).
- Mercury 3 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 decks from an analysis and marketing planning.
Head to head
Every fact side by side
| Mercury 3 | GPT-6 Luna | |
|---|---|---|
| Price per million tokens | $0.20 in · $0.70 out | $0.10 in · $0.50 out |
| Blended (3 in : 1 out) | $0.33 | $0.20 |
| Cheapest host, blended | – | $0.20 (Azure) |
| Cost per deck (DeckBench) | $0.012 | $0.009 |
| Cached input, per million | $0.020 cached input | $0.010 cached input |
| Intelligence Index | – | 38.1 |
| Spring Prompt overall | 39 | 40 |
| Output speed, tokens a second | – | 127 |
| Context window | 1,000,000 tokens | 1,050,000 tokens |
| Released | 19 Oct 2026 (newer) | 22 Sep 2026 |
| Weights | Closed | Closed |
| Developer based in | the US | 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.
Their slides, side by side
The same brief for both: Manchester store investment review for Hearthside Coffee, an invented company. First slide of each deck from DeckBench.
From our research
- Mercury 3 first look: very fast with thinking off, too slow with it on 20 Oct 2026
- Best AI for Product Descriptions: Accuracy, Cost and Real Examples 20 Oct 2026
- DeepSeek-V4.1-Flash-1017 review: better at analysis, much worse at sticking to the product data 19 Oct 2026
- MiniMax M3.1 Flash first look: careful with product data, unreliable on long jobs 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 | Mercury 3 | GPT-6 Luna |
|---|---|---|
| BulletBench measured by Spring Prompt · 3 results | ||
| Cost per game · Bullet 60sUS dollars, lower is better 7 · 5 of 32 measured | $0.0020no reasoning | $0.0016no reasoning |
| Ladder Elo · Bullet 60sladder Elo, higher is better 15 · 25 of 32 measured | 491no reasoning | 249no reasoning |
| Games lost on time · Bullet 60s% of games, lower is better 1 · 20 of 32 measured | 0.0%no reasoning | 50.0%no reasoning |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 4 · 2 of 24 measured | $0.0116 | $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. 18 · 7 of 24 measured | 812 | 1,343 |
| Accurate decks% of tasks, higher is better 20 · 9 of 24 measured | 0.0% | 50.0% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 18 · 3 of 24 measured | 26.1 | 53.4 |
| Audience scorescore out of 100, higher is better 21 · 3 of 24 measured | 43.2 | 68.3 |
| Business scorescore out of 100, higher is better 18 · 3 of 24 measured | 17.6 | 54.8 |
Show all 31 results (22 more)
| Measure | Mercury 3 | GPT-6 Luna |
|---|---|---|
| BulletBench measured by Spring Prompt · 2 results | ||
| Invalid moves · Bullet 60s% of moves, lower is better 1 · 1 of 32 measured | 0.0%low reasoning | 0.0%no reasoning |
| Median move time · Bullet 60smilliseconds, lower is better 3 · 20 of 32 measured | 0.4 sno reasoning | 1.5 sno reasoning |
| DeckBench measured by Spring Prompt · 14 results | ||
| Caveat dropped% of tasks, lower is better 12 · 1 of 24 measured | 16.7% | 0.0% |
| Clean layout% of tasks, higher is better 6 · 6 of 24 measured | 66.7% | 66.7% |
| Design quality% of the maximum, higher is better 16 · 5 of 24 measured | 58.5% | 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 17 · 1 of 24 measured | 16.7% | 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. 16 · 9 of 24 measured | 44.0% | 75.7% |
| Layout defects% of tasks, lower is better 6 · 6 of 24 measured | 33.3% | 33.3% |
| Misleading metric used% of tasks, lower is better 22 · 1 of 24 measured | 16.7% | 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 22 · 9 of 24 measured | 83.3% | 16.7% |
| Slides needing work% of tasks, lower is better 7 · 2 of 24 measured | 33.3% | 16.7% |
| Time per deckseconds, lower is better 9 · 14 of 24 measured | 83.3 | 101 |
| Unsupported claims% of tasks, lower is better 11 · 1 of 24 measured | 16.7% | 0.0% |
| Unsupported numbers% of tasks, lower is better 20 · 1 of 24 measured | 16.7% | 0.0% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 19 · 4 of 24 measured | 15.1 | 35.8 |
| Contribution profitsimulated US dollars, higher is better 18 · 4 of 24 measured | $104,868 | $738,879 |
| Cost of a runUS dollars, lower is better 6 · 2 of 24 measured | $0.0533 | $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 23 · 12 of 24 measured | 53.8 | 54.8 |
| Return on ad spendprofit per $1 spent, higher is better 18 · 4 of 24 measured | $0.10 | $0.66 |
Compare them with other models
- Mercury 3 vs Claude Opus 5.5Highest-scoring overall available today
- Mercury 3 vs GLM-5.3Just below overall
- 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 Mercury 3 better than GPT-6 Luna?
They score about the same, so pick GPT-6 Luna: it costs 26% less per deck on DeckBench.
Which is cheaper, Mercury 3 or GPT-6 Luna?
GPT-6 Luna is cheaper to run: $0.009 against $0.012 per deck on DeckBench (26% less).
Which is better for decks from an analysis, Mercury 3 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 18 for Mercury 3.
Which is better for marketing planning, Mercury 3 or GPT-6 Luna?
GPT-6 Luna ranks 3 of 24 models on sale for planning a year of ad spend without overspending, against 18 for Mercury 3.
Which is better for speed under pressure, Mercury 3 or GPT-6 Luna?
Mercury 3 ranks 10 of 27 models on sale for good decisions against a real clock (fast chess), against 20 for GPT-6 Luna.
Which has the bigger context window, Mercury 3 or GPT-6 Luna?
GPT-6 Luna: 1,050,000 tokens, against 1,000,000 for Mercury 3.
Run Mercury 3 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.