Ternary Bonsai 2 27B vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 2.5x less per output token than Ternary Bonsai 2 27B ($0.20 vs $0.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
Ternary Bonsai 2 27B or Qwen2.5 7B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Ternary Bonsai 2 27B for a larger context window (262K against 33K tokens).
No benchmark has tested both yet. Ternary Bonsai 2 27B has none yet; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). 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
Long documents
Listed context window
Ternary Bonsai 2 27B
Ternary Bonsai 2 27B takes 262K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
Ternary Bonsai 2 27B $0.0003, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Ternary Bonsai 2 27B not benchmarked, Qwen2.5 7B Instruct 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
- Ternary Bonsai 2 27B $0.50 against Qwen2.5 7B Instruct $0.20 per million output tokens, 2.5x apart.
- Context window
- Ternary Bonsai 2 27B takes 262K tokens, Qwen2.5 7B Instruct 33K.
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.
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ternary Bonsai 2 27B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ternary Bonsai 2 27B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ternary Bonsai 2 27B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 22 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ternary Bonsai 2 27B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ternary Bonsai 2 27B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
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
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Ternary Bonsai 2 27B costs less
Agent loop
200K input + 10K output tokens
Ternary Bonsai 2 27B 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 | PrismML Ternary Bonsai 2 27B | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | Prism-ml | Qwen |
| Weights | Open weights | Open weights |
| Released | September 18, 2026 | October 16, 2024 |
| Context window | 262K | 33K |
| Input $/Mtok | $0.075 | $0.10 |
| Output $/Mtok | $0.50 | $0.20 |
| 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 Ternary Bonsai 2 27B better than Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ternary Bonsai 2 27B for a larger context window (262K against 33K tokens).
Which is cheaper, Ternary Bonsai 2 27B or Qwen2.5 7B Instruct?
Ternary Bonsai 2 27B costs $0.075 per million input tokens and $0.50 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Ternary Bonsai 2 27B or Qwen2.5 7B Instruct?
Ternary Bonsai 2 27B, with 262K tokens against 33K.
Has any benchmark tested both Ternary Bonsai 2 27B and Qwen2.5 7B Instruct?
No benchmark has tested both yet. Ternary Bonsai 2 27B has none yet; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 22, 2026.