Llama 3.3 70B Instruct vs Qwen2.5 Coder 32B Instruct
Llama 3.3 70B Instruct leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 7.7 to 6.7, and Llama 3.3 70B Instruct costs 1.4x less per output token ($0.72 vs $1.00 per million). Updated October 22, 2026; 29 benchmarks have a result for both.
Llama 3.3 70B Instruct or Qwen2.5 Coder 32B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 1.4x cheaper ($0.72 vs $1.00 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 29
- Llama 3.3 70B Instruct only
- 23
- Qwen2.5 Coder 32B Instruct only
- 6
- 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
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads, 7.7 against 6.7.
Same board, both measured
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Knowledge, 8.0 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena Text: entertainment and media: Llama 3.3 70B Instruct 1241 against Qwen2.5 Coder 32B Instruct 1150, 15.1 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 1.4x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, Qwen2.5 Coder 32B Instruct 33K.
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 | Llama 3.3 70B Instruct | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 10 | 1 | ||
| All shared boards | 26 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | 29.5% | Qwen2.5 Coder 32B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | 1276 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: software and IT | 1273 | 1261 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 6.7 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | 127.3 | 119.5 | Llama 3.3 70B Instruct |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 31.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 41.7% | Llama 3.3 70B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 3.5% | Llama 3.3 70B Instruct |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1251 | Llama 3.3 70B Instruct |
| LMArena Text: expert | 1225 | 1221 | Llama 3.3 70B Instruct |
| LMArena Text: hard prompts in English | 1277 | 1269 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1267 | 1251 | Llama 3.3 70B Instruct |
| LMArena Text: mathematical work | 1267 | 1243 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | 63.5% | Llama 3.3 70B Instruct |
| MMLU | 86.3% | 79.1% | Llama 3.3 70B Instruct |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1284 | 1221 | Llama 3.3 70B Instruct |
| LMArena Text: medicine and healthcare | 1275 | 1187 | Llama 3.3 70B Instruct |
| LMArena Text: legal and government | 1284 | 1257 | Llama 3.3 70B Instruct |
| LMArena Text: business and finance | 1243 | 1224 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index101
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 47.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| Long context reasoning (AA-LCR) | 15.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| DTBench | 59.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMCA | 17.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1274 | 1230 | Llama 3.3 70B Instruct |
| LMArena Text: instruction following | 1242 | 1223 | Llama 3.3 70B Instruct |
| LMArena Text: creative writing | 1250 | 1174 | Llama 3.3 70B Instruct |
| LMArena Text: English | 1303 | 1248 | Llama 3.3 70B Instruct |
| LMArena Text: non-English | 1236 | 1205 | Llama 3.3 70B Instruct |
| LMArena Text: Chinese | 1217 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: German | 1251 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | 1150 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | 1142 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | 1282 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | 1269 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1252 | 1228 | Llama 3.3 70B Instruct |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | 1222 | Llama 3.3 70B Instruct |
| LMArena Text: longer queries | 1255 | 1251 | Llama 3.3 70B Instruct |
| LMArena Text: writing and language | 1255 | 1191 | Llama 3.3 70B Instruct |
| LMArena Text: entertainment and media | 1241 | 1150 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen2.5 Coder 32B 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
Llama 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.3 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.3 70B Instruct 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 | Meta Llama 3.3 70B Instruct | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | December 6, 2024 | November 11, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.45 | $0.66 |
| Output $/Mtok | $0.72 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.3 70B Instruct better than Qwen2.5 Coder 32B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 1.4x cheaper ($0.72 vs $1.00 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Llama 3.3 70B Instruct or Qwen2.5 Coder 32B Instruct?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Qwen2.5 Coder 32B Instruct?
Llama 3.3 70B Instruct, with 131K tokens against 33K.
How many benchmarks is the Llama 3.3 70B Instruct vs Qwen2.5 Coder 32B Instruct comparison based on?
29 benchmarks have a result for both models as of October 22, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.