Mercury 2 vs Llama 3.3 70B Instruct
Mercury 2 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 10.8 to 7.7, at about the same output price ($0.75 vs $0.72 per million). Updated October 21, 2026; 28 benchmarks have a result for both.
Mercury 2 or Llama 3.3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 28
- Mercury 2 only
- 14
- Llama 3.3 70B Instruct only
- 24
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Mercury 2
Mercury 2 leads, 10.8 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
No call
Only Mercury 2 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
Mercury 2 $0.0006, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- Mercury 2 not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Agentic work, 44.2 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Mercury 2 70.8% against Llama 3.3 70B Instruct 26.6%, 44.2 points apart on a 0 to 100 scale.
- Output price
- Mercury 2 $0.75 against Llama 3.3 70B Instruct $0.72 per million output tokens, 1.0x apart.
- Context window
- Mercury 2 takes 128K tokens, Llama 3.3 70B Instruct 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Mercury 2 | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Knowledge | 3 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 28 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| LMArena WebDev | 1171 | Not measured | Only Mercury 2 measured |
| AA Coding Index | 31.1 | 11.9 | Mercury 2 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 28.8% | Only Llama 3.3 70B Instruct measured |
| SciCode | 37.7% | Not measured | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 3.0% | Mercury 2 |
| WeirdML | 43.2% | 14.4% | Mercury 2 |
| ALE-Bench | 785.6 | Not measured | Only Mercury 2 measured |
| LMArena Text: coding | 1390 | 1268 | Mercury 2 |
| LMArena Text: software and IT | 1385 | 1273 | Mercury 2 |
| LMArena WebDev: React | 1160 | Not measured | Only Mercury 2 measured |
| LMArena WebDev: HTML | 1199 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 7.7 | Mercury 2 |
| Epoch Capabilities Index | Not measured | 127.3 | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Mercury 2 measured |
| τ²-Bench Telecom | 70.8% | 26.6% | Mercury 2 |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 31.9% | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 77.0% | 49.8% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 3.6% | Mercury 2 |
| GPQA Diamond (Epoch AI run) | Not measured | 47.4% | Only Llama 3.3 70B Instruct measured |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1362 | 1257 | Mercury 2 |
| LMArena Text: expert | 1358 | 1225 | Mercury 2 |
| LMArena Text: hard prompts in English | 1374 | 1277 | Mercury 2 |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 7.7 | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 7.7% | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | Not measured | 5.1% | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | Not measured | 1267 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1267 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 71.3% | Only Llama 3.3 70B Instruct measured |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1343 | 1284 | Mercury 2 |
| LMArena Text: medicine and healthcare | 1363 | 1275 | Mercury 2 |
| LMArena Text: legal and government | Not measured | 1284 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | 1349 | 1243 | Mercury 2 |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | Not measured | Only Mercury 2 measured |
| Design Arena: UI components | 998 | Not measured | Only Mercury 2 measured |
| Design Arena: game dev | 985 | Not measured | Only Mercury 2 measured |
| Design Arena: data viz | 1009 | Not measured | Only Mercury 2 measured |
| Design Arena: 3D | 983 | Not measured | Only Mercury 2 measured |
| Design Arena: all code categories | 1022 | Not measured | Only Mercury 2 measured |
| Design Arena: SVG | 998 | Not measured | Only Mercury 2 measured |
| Design Arena: ASCII art | 988 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 69.8% | 47.1% | Mercury 2 |
| Long context reasoning (AA-LCR) | 43.7% | 15.7% | Mercury 2 |
| DTBench | Not measured | 59.5% | Only Llama 3.3 70B Instruct measured |
| LMCA | Not measured | 17.5% | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1354 | 1274 | Mercury 2 |
| LMArena Text: instruction following | 1328 | 1242 | Mercury 2 |
| LMArena Text: creative writing | 1286 | 1250 | Mercury 2 |
| LMArena Text: English | 1378 | 1303 | Mercury 2 |
| LMArena Text: non-English | 1330 | 1236 | Mercury 2 |
| LMArena Text: Chinese | 1414 | 1217 | Mercury 2 |
| LMArena Text: German | Not measured | 1251 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | Not measured | 1150 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | Not measured | 1142 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | Not measured | 1282 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | Not measured | 1269 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1304 | 1252 | Mercury 2 |
| LMArena Text: Polish | Not measured | 1259 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1357 | 1280 | Mercury 2 |
| LMArena Text: longer queries | 1329 | 1255 | Mercury 2 |
| LMArena Text: writing and language | 1300 | 1255 | Mercury 2 |
| LMArena Text: entertainment and media | 1314 | 1241 | Mercury 2 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mercury 2 costs less
Repository review
50K input + 3K output tokens
Mercury 2 costs less
Agent loop
200K input + 10K output tokens
Mercury 2 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Inception Mercury 2 | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | Inception | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | March 4, 2026 | December 6, 2024 |
| Context window | 128K | 131K |
| Input $/Mtok | $0.25 | $0.45 |
| Output $/Mtok | $0.75 | $0.72 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2 better than Llama 3.3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Mercury 2 or Llama 3.3 70B Instruct?
Mercury 2 costs $0.25 per million input tokens and $0.75 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct, with 131K tokens against 128K.
How many benchmarks is the Mercury 2 vs Llama 3.3 70B Instruct comparison based on?
28 benchmarks have a result for both models as of October 21, 2026. Mercury 2's coding index rests on 1 independent benchmark and Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.