GPT-5.6 Sol vs Llama 3.3 70B Instruct
GPT-5.6 Sol leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 47.0 to 7.7, and Llama 3.3 70B Instruct costs 27.8x less per output token ($0.72 vs $20.00 per million). Updated October 21, 2026; 44 benchmarks have a result for both.
vs boards won of 44 shared
GPT-5.6 Sol or Llama 3.3 70B Instruct: which to pick
Only GPT-5.6 Sol has a coding score, 73.5 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 27.8x cheaper ($0.72 vs $20.00 per million).
Model AGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved taskModel BLlama 3.3 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 44
- GPT-5.6 Sol only
- 95
- Llama 3.3 70B Instruct only
- 8
- Like-for-like categories
- 7 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
Only GPT-5.6 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Sol has a AA Agentic Index result, 50.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.6 Sol has a Design Arena: websites result, 1321.
Directional only
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
GPT-5.6 Sol $0.014, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Math, 55.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GPT-5.6 Sol 100.0% against Llama 3.3 70B Instruct 5.1%, 94.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Llama 3.3 70B Instruct $0.72 per million output tokens, 27.8x apart.
- Context window
- GPT-5.6 Sol takes 1.05M 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 | GPT-5.6 Sol | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 44 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | Not measured | Only GPT-5.6 Sol measured |
| AA Coding Index | 77.4 | 11.9 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | Not measured | Only GPT-5.6 Sol measured |
| SWE-bench Verified (Vals AI) | 96.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 4 (Vals AI) | 37.9% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 80.5% | Not measured | Only GPT-5.6 Sol measured |
| Code migration (Vals AI) | 52.9% | Not measured | Only GPT-5.6 Sol measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | Not measured | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 28.8% | Only Llama 3.3 70B Instruct measured |
| SciCode | 57.8% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | 3.0% | GPT-5.6 Sol |
| FrontierCode | 47.5% | Not measured | Only GPT-5.6 Sol measured |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | 14.4% | GPT-5.6 Sol |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1496 | 1268 | GPT-5.6 Sol |
| LMArena Text: software and IT | 1489 | 1273 | GPT-5.6 Sol |
| LMArena WebDev: React | 1620 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: HTML | 1618 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: from an image | 1609 | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 7.7 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | 127.3 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building web apps | 1245 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | 1248 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | 1239 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | 1260 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | 1267 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1286 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | 1241 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench research | 54.5% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench long agentic | 48.5% | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | Not measured | Only GPT-5.6 Sol measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | 48.1% | Not measured | Only GPT-5.6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 72.3% | Not measured | Only GPT-5.6 Sol measured |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | 26.6% | GPT-5.6 Sol |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | 23.0% | GPT-5.6 Sol |
| PostTrainBench | 36.2% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol 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 12 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench reasoning | 91.7% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 49.8% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 3.6% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | 47.4% | GPT-5.6 Sol |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | 19.9% | GPT-5.6 Sol |
| Chess puzzles | 59.0% | Not measured | Only GPT-5.6 Sol measured |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | 1257 | GPT-5.6 Sol |
| LMArena Text: expert | 1517 | 1225 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1477 | 1277 | GPT-5.6 Sol |
| LMArena Vision: diagrams | 1305 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1305 | Not measured | Only GPT-5.6 Sol measured |
| Kagi LLM Benchmark | 67.0% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | 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 | 100.0% | 5.1% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | 82.9% | Not measured | Only GPT-5.6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-5.6 Sol measured |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1473 | 1267 | GPT-5.6 Sol |
| LMArena Text: mathematical work | 1496 | 1267 | GPT-5.6 Sol |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | 87.0% | Not measured | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | 64.4% | Not measured | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | 74.8% | Not measured | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | Not measured | Only GPT-5.6 Sol measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 71.3% | Only Llama 3.3 70B Instruct measured |
| SimpleQA Verified | 69.7% | Not measured | Only GPT-5.6 Sol measured |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1455 | 1284 | GPT-5.6 Sol |
| LMArena Text: medicine and healthcare | 1431 | 1275 | GPT-5.6 Sol |
| LMArena Text: legal and government | 1476 | 1284 | GPT-5.6 Sol |
| LMArena Text: business and finance | 1457 | 1243 | GPT-5.6 Sol |
| FACTS Benchmark Suite | 52.4% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: UI components | 1336 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: game dev | 1368 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: data viz | 1322 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: 3D | 1386 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: all code categories | 1341 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: SVG | 1316 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | 1304 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench design | 81.6% | Not measured | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | GPT-5.6 Sol | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | Not measured | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | 52.6% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | Not measured | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | 47.1% | GPT-5.6 Sol |
| Long context reasoning (AA-LCR) | 84.0% | 15.7% | GPT-5.6 Sol |
| DTBench | 95.7% | 59.5% | GPT-5.6 Sol |
| LMCA | 58.5% | 17.5% | GPT-5.6 Sol |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | 1274 | GPT-5.6 Sol |
| LMArena Text: instruction following | 1483 | 1242 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1449 | 1250 | GPT-5.6 Sol |
| LMArena Vision | 1281 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1457 | 1303 | GPT-5.6 Sol |
| LMArena Text: non-English | 1451 | 1236 | GPT-5.6 Sol |
| LMArena Text: Chinese | 1525 | 1217 | GPT-5.6 Sol |
| LMArena Text: German | 1476 | 1251 | GPT-5.6 Sol |
| LMArena Text: Japanese | 1472 | 1150 | GPT-5.6 Sol |
| LMArena Text: Korean | 1442 | 1142 | GPT-5.6 Sol |
| LMArena Text: French | 1472 | 1282 | GPT-5.6 Sol |
| LMArena Text: Spanish | 1443 | 1269 | GPT-5.6 Sol |
| LMArena Text: Russian | 1471 | 1252 | GPT-5.6 Sol |
| LMArena Text: Polish | 1459 | 1259 | GPT-5.6 Sol |
| LMArena Text: multi-turn | 1461 | 1280 | GPT-5.6 Sol |
| LMArena Text: longer queries | 1481 | 1255 | GPT-5.6 Sol |
| LMArena Text: writing and language | 1462 | 1255 | GPT-5.6 Sol |
| LMArena Text: entertainment and media | 1450 | 1241 | GPT-5.6 Sol |
| LMArena Vision: English | 1280 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1313 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1246 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol | 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
GPT-5.6 Sol$0.014Fits in one request
Llama 3.3 70B Instruct$0.0008Fits in one request
Llama 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol$0.260Fits in one request
Llama 3.3 70B Instruct$0.025Fits in one request
Llama 3.3 70B Instruct costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol$1.00Fits in one request
Llama 3.3 70B Instruct$0.097Exceeds the context window
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 | OpenAI GPT-5.6 Sol | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | December 6, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $4.00 | $0.45 |
| Output $/Mtok | $20.00 | $0.72 |
| Coding index | 73.5 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $1.20 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Llama 3.3 70B Instruct for coding?
Only GPT-5.6 Sol has a coding score, 73.5 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 27.8x cheaper ($0.72 vs $20.00 per million).
Which is cheaper, GPT-5.6 Sol or Llama 3.3 70B Instruct?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, GPT-5.6 Sol or Llama 3.3 70B Instruct?
GPT-5.6 Sol, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.6 Sol vs Llama 3.3 70B Instruct comparison based on?
44 benchmarks have a result for both models as of October 21, 2026. GPT-5.6 Sol's coding index rests on 6 independent benchmarks 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.