1-bit Bonsai 1.7B vs MiniMax M2.7

Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.

Benchmark data for one or both models is coming soon. This page currently shows metadata and pricing where BenchLM has it, and score-level comparisons will populate as public benchmark results land.
Agentic
Coding
Multimodal & Grounded
Reasoning
Knowledge
Instruction Following
Multilingual
Mathematics

1-bit Bonsai 1.7B· MiniMax M2.7

Quick Verdict

Benchmark data for 1-bit Bonsai 1.7B and MiniMax M2.7 is coming soon on BenchLM.

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

MiniMax M2.7 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 1.7B. MiniMax M2.7 has the larger context window at 200K, compared with 32K for 1-bit Bonsai 1.7B.

Operational tradeoffs

PriceFree*$0.30 / $1.20
SpeedN/A45 t/s
TTFTN/A2.53s
Context32K200K

Decision framing

BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.

Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.

Benchmark1-bit Bonsai 1.7BMiniMax M2.7
Agentic
Terminal-Bench 2.057%
Toolathlon46.3%
MLE-Bench Lite66.6%
MM-ClawBench62.7%
Coding
SWE-bench Pro56.2%
SWE Multilingual76.5%
Multi-SWE Bench52.7%
VIBE-Pro55.6%
NL2Repo39.8%
Multimodal & Grounded
GDPval-AA1495
Reasoning
MuSR45.1%
Knowledge
GPQA20.7%
Instruction Following
IFEval63%
Multilingual
Coming soon
Mathematics
MATH-50034.4%
Frequently Asked Questions (3)

Can I compare 1-bit Bonsai 1.7B and MiniMax M2.7 on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for 1-bit Bonsai 1.7B and MiniMax M2.7 today?

1-bit Bonsai 1.7B: $0.00 input / $0.00 output per 1M tokens MiniMax M2.7: $0.30 input / $1.20 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

Last updated: March 31, 2026

Weekly LLM Benchmark Digest

Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.

Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.