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Ternary Bonsai 2 27B vs Qwen3.5-9B

Qwen3.5-9B costs 3.3x less per output token than Ternary Bonsai 2 27B ($0.15 vs $0.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.

P Ternary Bonsai 2 27BPrism-ml
Coding
No data
Overall
No data
Output $/M
$0.50
Qwen3.5-9BQwen
Coding
No data
Overall
No data
Output $/M
$0.15
vs $0.50 vs $0.15 per 1M output tokens. Qwen3.5-9B is 3.3x cheaper.

Ternary Bonsai 2 27B or Qwen3.5-9B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

No benchmark has tested both yet. Ternary Bonsai 2 27B has none yet; Qwen3.5-9B on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis 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.

At a glance

Coding index
Ternary Bonsai 2 27B not benchmarked, Qwen3.5-9B 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 Qwen3.5-9B $0.15 per million output tokens, 3.3x apart.
Context window
Both take 262K tokens.

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 Index4 boards measured
Coding benchmarks for Ternary Bonsai 2 27B and Qwen3.5-9B
BenchmarkTernary Bonsai 2 27BQwen3.5-9BLeader
AA Coding IndexNot measured23.5Only Qwen3.5-9B measured
SciCodeNot measured29.5%Only Qwen3.5-9B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured24.2%Only Qwen3.5-9B measured
Terminal-BenchNot measured9.2%Only Qwen3.5-9B measured
Neither model has a result on the other 39 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Intelligence benchmarks for Ternary Bonsai 2 27B and Qwen3.5-9B
BenchmarkTernary Bonsai 2 27BQwen3.5-9BLeader
AA Intelligence IndexNot measured13.3Only Qwen3.5-9B measured
Epoch Capabilities IndexNot measured139.3Only Qwen3.5-9B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Agentic work benchmarks for Ternary Bonsai 2 27B and Qwen3.5-9B
BenchmarkTernary Bonsai 2 27BQwen3.5-9BLeader
τ²-Bench TelecomNot measured86.8%Only Qwen3.5-9B measured
Neither model has a result on the other 41 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
Reasoning benchmarks for Ternary Bonsai 2 27B and Qwen3.5-9B
BenchmarkTernary Bonsai 2 27BQwen3.5-9BLeader
GPQA Diamond (Artificial Analysis run)Not measured80.6%Only Qwen3.5-9B measured
Humanity's Last Exam (Artificial Analysis run)Not measured14.9%Only Qwen3.5-9B measured
GPQA Diamond (Epoch AI run)Not measured79.0%Only Qwen3.5-9B measured
Neither model has a result on the other 20 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Math benchmarks for Ternary Bonsai 2 27B and Qwen3.5-9B
BenchmarkTernary Bonsai 2 27BQwen3.5-9BLeader
OTIS Mock AIMENot measured61.7%Only Qwen3.5-9B measured
MathArenaNot measured13.0%Only Qwen3.5-9B measured
Neither model has a result on the other 12 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
General benchmarks for Ternary Bonsai 2 27B and Qwen3.5-9B
BenchmarkTernary Bonsai 2 27BQwen3.5-9BLeader
IFBenchNot measured66.7%Only Qwen3.5-9B measured
Long context reasoning (AA-LCR)Not measured70.0%Only Qwen3.5-9B measured
DTBenchNot measured71.2%Only Qwen3.5-9B measured
LMCANot measured24.5%Only Qwen3.5-9B measured
Neither model has a result on the other 44 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
Qwen3.5-9B$0.0002Fits in one request

Qwen3.5-9B costs less

Repository review

50K input + 3K output tokens

Ternary Bonsai 2 27B$0.0053Fits in one request
Qwen3.5-9B$0.0054Fits 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
Qwen3.5-9B$0.021Fits in one request

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 Qwen3.5-9B
SpecificationPrismML Ternary Bonsai 2 27BQwen3.5-9B
MakerPrism-mlQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 18, 2026March 10, 2026
Context window262K262K
Input $/Mtok$0.075$0.10
Output $/Mtok$0.50$0.15
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 Qwen3.5-9B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Ternary Bonsai 2 27B or Qwen3.5-9B?

Ternary Bonsai 2 27B costs $0.075 per million input tokens and $0.50 per million output tokens; Qwen3.5-9B costs $0.10 and $0.15. Cost per solved task is known for neither of them.

Which has the bigger context window, Ternary Bonsai 2 27B or Qwen3.5-9B?

Neither: both take 262K tokens.

Has any benchmark tested both Ternary Bonsai 2 27B and Qwen3.5-9B?

No benchmark has tested both yet. Ternary Bonsai 2 27B has none yet; Qwen3.5-9B on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead. Data as of October 22, 2026.

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