Llama 3.3 70B Instruct vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 39.9 to 7.7, and Llama 3.3 70B Instruct costs 8.3x less per output token ($0.72 vs $6.00 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
Llama 3.3 70B Instruct or Qwen3.8 2.4T A95B: 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 8.3x cheaper ($0.72 vs $6.00 per million) and work measured by AA Coding Index or AA Intelligence Index, where it leads.
- Shared results
- 5
- Llama 3.3 70B Instruct only
- 47
- Qwen3.8 2.4T A95B only
- 4
- Like-for-like categories
- 4 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
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 2.4T A95B has a AA Agentic Index result, 50.1.
Directional only
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, Qwen3.8 2.4T A95B $0.0050 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- General, 64.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Llama 3.3 70B Instruct 15.7% against Qwen3.8 2.4T A95B 80.3%, 64.7 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 8.3x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, Qwen3.8 2.4T A95B 1.05M.
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 | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | 71.9 | Qwen3.8 2.4T A95B |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| SciCode | Not measured | 54.1% | Only Qwen3.8 2.4T A95B measured |
| 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 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: software and IT | 1273 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 39.9 | Qwen3.8 2.4T A95B |
| Epoch Capabilities Index | 127.3 | Not measured | Only Llama 3.3 70B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.1 | Only Qwen3.8 2.4T A95B measured |
| 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 |
| MCP Atlas | Not measured | 84.5% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 42.4% | Qwen3.8 2.4T A95B |
| 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 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: expert | 1225 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts in English | 1277 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | 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 |
| LMArena Text: math | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| MathArena | Not measured | 31.6% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: medicine and healthcare | 1275 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: legal and government | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | 1243 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| IFBench | 47.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| Long context reasoning (AA-LCR) | 15.7% | 80.3% | Qwen3.8 2.4T A95B |
| 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 |
| LMArena Text | 1274 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: instruction following | 1242 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: creative writing | 1250 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: English | 1303 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: non-English | 1236 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Chinese | 1217 | Not measured | Only Llama 3.3 70B Instruct measured |
| 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 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: longer queries | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: writing and language | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: entertainment and media | 1241 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Qwen3.8 2.4T A95B | 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 | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | December 6, 2024 | August 12, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.45 | $2.00 |
| Output $/Mtok | $0.72 | $6.00 |
| 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 Qwen3.8 2.4T A95B 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 8.3x cheaper ($0.72 vs $6.00 per million) and work measured by AA Coding Index or AA Intelligence Index, where it leads.
Which is cheaper, Llama 3.3 70B Instruct or Qwen3.8 2.4T A95B?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B, with 1.05M tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs Qwen3.8 2.4T A95B comparison based on?
5 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 Qwen3.8 2.4T A95B's on 0. Where only one model has a result, the page says so instead of naming a winner.