Enter opens the highlighted result. Esc closes.

Ternary Bonsai 2 27B vs Qwen2.5 VL 72B Instruct

Ternary Bonsai 2 27B costs 2.0x less per output token than Qwen2.5 VL 72B Instruct ($0.50 vs $1.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.

Replace Ternary Bonsai 2 27B Replace Qwen2.5 VL 72B Instruct
P Ternary Bonsai 2 27BPrism-ml
Coding
No data
Overall
No data
Output $/M
$0.50
Qwen2.5 VL 72B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
vs $0.50 vs $1.00 per 1M output tokens. Ternary Bonsai 2 27B is 2.0x cheaper.

Ternary Bonsai 2 27B or Qwen2.5 VL 72B 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 output tokens 2.0x cheaper ($0.50 vs $1.00 per million) and a larger context window (262K against 128K tokens).

Model ATernary Bonsai 2 27BPrism-mlNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 VL 72B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task

No benchmark has tested both yet. Ternary Bonsai 2 27B has none yet; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. 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.

At a glance

Coding index
Ternary Bonsai 2 27B not benchmarked, Qwen2.5 VL 72B 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 VL 72B Instruct $1.00 per million output tokens, 2.0x apart.
Context window
Ternary Bonsai 2 27B takes 262K tokens, Qwen2.5 VL 72B Instruct 128K.

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.

Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Agentic work benchmarks for Ternary Bonsai 2 27B and Qwen2.5 VL 72B Instruct
BenchmarkTernary Bonsai 2 27BQwen2.5 VL 72B InstructLeader
OSWorldNot measured5.0%Only Qwen2.5 VL 72B 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
Reasoning benchmarks for Ternary Bonsai 2 27B and Qwen2.5 VL 72B Instruct
BenchmarkTernary Bonsai 2 27BQwen2.5 VL 72B InstructLeader
Kagi LLM BenchmarkNot measured36.0%Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 22 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Knowledge benchmarks for Ternary Bonsai 2 27B and Qwen2.5 VL 72B Instruct
BenchmarkTernary Bonsai 2 27BQwen2.5 VL 72B InstructLeader
GeoBenchNot measured62.0%Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 19 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
General benchmarks for Ternary Bonsai 2 27B and Qwen2.5 VL 72B Instruct
BenchmarkTernary Bonsai 2 27BQwen2.5 VL 72B InstructLeader
LMArena VisionNot measured1107Only Qwen2.5 VL 72B Instruct measured
LMArena Vision: EnglishNot measured1115Only Qwen2.5 VL 72B Instruct measured
LMArena Vision: ChineseNot measured1107Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 45 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

Ternary Bonsai 2 27B$0.0003Fits in one request
Qwen2.5 VL 72B Instruct$0.0013Fits in one request

Ternary Bonsai 2 27B costs less

Repository review

50K input + 3K output tokens

Ternary Bonsai 2 27B$0.0053Fits in one request
Qwen2.5 VL 72B Instruct$0.043Fits in one request

Ternary Bonsai 2 27B costs less

Agent loop

200K input + 10K output tokens

Ternary Bonsai 2 27B$0.020Fits in one request
Qwen2.5 VL 72B Instruct$0.170Exceeds the context window

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.

Specifications of Ternary Bonsai 2 27B and Qwen2.5 VL 72B Instruct
SpecificationPrismML Ternary Bonsai 2 27BQwen2.5 VL 72B Instruct
MakerPrism-mlQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 18, 2026February 1, 2025
Context window262K128K
Input $/Mtok$0.075$0.80
Output $/Mtok$0.50$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Ternary Bonsai 2 27B better than Qwen2.5 VL 72B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ternary Bonsai 2 27B for output tokens 2.0x cheaper ($0.50 vs $1.00 per million) and a larger context window (262K against 128K tokens).

Which is cheaper, Ternary Bonsai 2 27B or Qwen2.5 VL 72B Instruct?

Ternary Bonsai 2 27B costs $0.075 per million input tokens and $0.50 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Ternary Bonsai 2 27B or Qwen2.5 VL 72B Instruct?

Ternary Bonsai 2 27B, with 262K tokens against 128K.

Has any benchmark tested both Ternary Bonsai 2 27B and Qwen2.5 VL 72B Instruct?

No benchmark has tested both yet. Ternary Bonsai 2 27B has none yet; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead. Data as of October 22, 2026.

Compare next