Llama 3.1 8B Instruct vs Ternary Bonsai 2 27B
Llama 3.1 8B Instruct costs 6.3x less per output token than Ternary Bonsai 2 27B ($0.08 vs $0.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 17.9
- Output $/M
- $0.08
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.50
Llama 3.1 8B Instruct or Ternary Bonsai 2 27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.1 8B Instruct for output tokens 6.3x cheaper ($0.08 vs $0.50 per million).
No benchmark has tested both yet. Llama 3.1 8B Instruct has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Ternary Bonsai 2 27B has none yet. Compare them on price and context instead.
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
Ternary Bonsai 2 27B
Ternary Bonsai 2 27B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 8B Instruct
Llama 3.1 8B Instruct $0.0001, Ternary Bonsai 2 27B $0.0003 per turn.
List prices
At a glance
- Coding index
- Llama 3.1 8B Instruct not benchmarked, Ternary Bonsai 2 27B not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Llama 3.1 8B Instruct $0.08 against Ternary Bonsai 2 27B $0.50 per million output tokens, 6.3x apart.
- Context window
- Llama 3.1 8B Instruct takes 131K tokens, Ternary Bonsai 2 27B 262K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| AA Coding Index | 5.4 | Not measured | Only Llama 3.1 8B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 11.6% | Not measured | Only Llama 3.1 8B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | Not measured | Only Llama 3.1 8B Instruct measured |
| WeirdML | 1.7% | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: coding | 1195 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: software and IT | 1195 | Not measured | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.9 | Not measured | Only Llama 3.1 8B Instruct measured |
| Epoch Capabilities Index | 116.5 | Not measured | Only Llama 3.1 8B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 16.4% | Not measured | Only Llama 3.1 8B Instruct measured |
| BALROG | 15.1% | Not measured | Only Llama 3.1 8B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 25.8% | Not measured | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 25.9% | Not measured | Only Llama 3.1 8B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | Not measured | Only Llama 3.1 8B Instruct measured |
| GPQA Diamond (Epoch AI run) | 27.0% | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: hard prompts | 1175 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: expert | 1144 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: hard prompts in English | 1196 | Not measured | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| AA Math Index | 4.3 | Not measured | Only Llama 3.1 8B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 4.3% | Not measured | Only Llama 3.1 8B Instruct measured |
| OTIS Mock AIME | 1.7% | Not measured | Only Llama 3.1 8B Instruct measured |
| MATH level 5 | 22.9% | Not measured | Only Llama 3.1 8B Instruct measured |
| GSM8K | 82.4% | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: math | 1179 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: mathematical work | 1180 | Not measured | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 47.6% | Not measured | Only Llama 3.1 8B Instruct measured |
| MMLU | 56.1% | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: science | 1193 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: medicine and healthcare | 1182 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: legal and government | 1206 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: business and finance | 1166 | Not measured | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards measured
| Benchmark | Llama 3.1 8B Instruct | Ternary Bonsai 2 27B | Leader |
|---|---|---|---|
| IFBench | 28.6% | Not measured | Only Llama 3.1 8B Instruct measured |
| Long context reasoning (AA-LCR) | 18.0% | Not measured | Only Llama 3.1 8B Instruct measured |
| DTBench | 50.9% | Not measured | Only Llama 3.1 8B Instruct measured |
| LMCA | 5.4% | Not measured | Only Llama 3.1 8B Instruct measured |
| PIQA | 81.2% | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text | 1187 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: instruction following | 1159 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: creative writing | 1154 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: English | 1218 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: non-English | 1148 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Chinese | 1151 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: German | 1144 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Japanese | 1061 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Korean | 1053 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: French | 1178 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Spanish | 1169 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Russian | 1158 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: multi-turn | 1172 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: longer queries | 1181 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: writing and language | 1162 | Not measured | Only Llama 3.1 8B Instruct measured |
| LMArena Text: entertainment and media | 1145 | Not measured | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 27 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 | Meta Llama 3.1 8B Instruct | PrismML Ternary Bonsai 2 27B |
|---|---|---|
| Maker | Meta Llama | Prism-ml |
| Weights | Open weights | Open weights |
| Released | July 23, 2024 | September 18, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.05 | $0.075 |
| Output $/Mtok | $0.08 | $0.50 |
| 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 3.1 8B Instruct better than Ternary Bonsai 2 27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.1 8B Instruct for output tokens 6.3x cheaper ($0.08 vs $0.50 per million).
Which is cheaper, Llama 3.1 8B Instruct or Ternary Bonsai 2 27B?
Llama 3.1 8B Instruct costs $0.05 per million input tokens and $0.08 per million output tokens; Ternary Bonsai 2 27B costs $0.075 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.1 8B Instruct or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B, with 262K tokens against 131K.
Has any benchmark tested both Llama 3.1 8B Instruct and Ternary Bonsai 2 27B?
No benchmark has tested both yet. Llama 3.1 8B Instruct has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Ternary Bonsai 2 27B has none yet. Compare them on price and context instead. Data as of October 22, 2026.