GPT-5.6 Sol Pro vs Llama 3.1 8B Instruct
Llama 3.1 8B Instruct costs 250.0x less per output token than GPT-5.6 Sol Pro ($0.08 vs $20.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-5.6 Sol Pro or Llama 3.1 8B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 131K tokens).
- Shared results
- 3
- GPT-5.6 Sol Pro only
- 4
- Llama 3.1 8B Instruct only
- 48
- Like-for-like categories
- 2 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
No call
Only Llama 3.1 8B Instruct has a AA Intelligence Index result, 6.9.
Directional only
Long documents
Listed context window
GPT-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 8B Instruct
GPT-5.6 Sol Pro $0.014, Llama 3.1 8B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-5.6 Sol Pro not benchmarked, Llama 3.1 8B Instruct not benchmarked.
- Widest category
- Coding, 87.7 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: GPT-5.6 Sol Pro 89.4% against Llama 3.1 8B Instruct 1.7%, 87.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol Pro $20.00 against Llama 3.1 8B Instruct $0.08 per million output tokens, 250.0x apart.
- Context window
- GPT-5.6 Sol Pro takes 1.05M tokens, Llama 3.1 8B 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 Pro | Bars | Llama 3.1 8B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 5.4 | Only Llama 3.1 8B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 11.6% | Only Llama 3.1 8B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 0.8% | Only Llama 3.1 8B Instruct measured |
| WeirdML | 89.4% | 1.7% | GPT-5.6 Sol Pro |
| LMArena Text: coding | Not measured | 1195 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: software and IT | Not measured | 1195 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.9 | Only Llama 3.1 8B Instruct measured |
| Epoch Capabilities Index | Not measured | 116.5 | Only Llama 3.1 8B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 16.4% | Only Llama 3.1 8B Instruct measured |
| APEX-Agents | 51.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| BALROG | Not measured | 15.1% | Only Llama 3.1 8B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 25.8% | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 25.9% | Only Llama 3.1 8B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 5.3% | Only Llama 3.1 8B Instruct measured |
| GPQA Diamond (Epoch AI run) | Not measured | 27.0% | Only Llama 3.1 8B Instruct measured |
| SimpleBench | 71.7% | Not measured | Only GPT-5.6 Sol Pro measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-5.6 Sol Pro measured |
| LMArena Text: hard prompts | Not measured | 1175 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: expert | Not measured | 1144 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1196 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 4.3 | Only Llama 3.1 8B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 4.3% | Only Llama 3.1 8B Instruct measured |
| OTIS Mock AIME | Not measured | 1.7% | Only Llama 3.1 8B Instruct measured |
| FrontierMath, tier 4 | 80.5% | Not measured | Only GPT-5.6 Sol Pro measured |
| MATH level 5 | Not measured | 22.9% | Only Llama 3.1 8B Instruct measured |
| GSM8K | Not measured | 82.4% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: math | Not measured | 1179 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1180 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 47.6% | Only Llama 3.1 8B Instruct measured |
| MMLU | Not measured | 56.1% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: science | Not measured | 1193 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1182 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: legal and government | Not measured | 1206 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: business and finance | Not measured | 1166 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 28.6% | Only Llama 3.1 8B Instruct measured |
| Long context reasoning (AA-LCR) | Not measured | 18.0% | Only Llama 3.1 8B Instruct measured |
| DTBench | 96.0% | 50.9% | GPT-5.6 Sol Pro |
| LMCA | 59.2% | 5.4% | GPT-5.6 Sol Pro |
| PIQA | Not measured | 81.2% | Only Llama 3.1 8B Instruct measured |
| LMArena Text | Not measured | 1187 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: instruction following | Not measured | 1159 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: creative writing | Not measured | 1154 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: English | Not measured | 1218 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: non-English | Not measured | 1148 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Chinese | Not measured | 1151 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: German | Not measured | 1144 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Japanese | Not measured | 1061 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Korean | Not measured | 1053 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: French | Not measured | 1178 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Spanish | Not measured | 1169 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Russian | Not measured | 1158 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1172 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: longer queries | Not measured | 1181 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: writing and language | Not measured | 1162 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1145 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Llama 3.1 8B 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 3.1 8B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.1 8B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 8B 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 Pro | Meta Llama 3.1 8B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | July 23, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $4.00 | $0.05 |
| Output $/Mtok | $20.00 | $0.08 |
| 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 GPT-5.6 Sol Pro better than Llama 3.1 8B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 131K tokens).
Which is cheaper, GPT-5.6 Sol Pro or Llama 3.1 8B Instruct?
GPT-5.6 Sol Pro costs $4.00 per million input tokens and $20.00 per million output tokens; Llama 3.1 8B Instruct costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.6 Sol Pro or Llama 3.1 8B Instruct?
GPT-5.6 Sol Pro, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.6 Sol Pro vs Llama 3.1 8B Instruct comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol Pro's coding index rests on 0 independent benchmarks and Llama 3.1 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.