AI tool comparison
Qwen3-Coder-Next vs Ternary Bonsai
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Open-Weight Models
Qwen3-Coder-Next
80B MoE coding agent, 3B active params, Apache 2.0, runs on consumer GPU
75%
Panel ship
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Community
Free
Entry
Qwen3-Coder-Next is Alibaba Qwen team's open-weight coding agent model — 80B total parameters but only 3B active via a Mixture-of-Experts architecture, making it runnable on consumer hardware (quantized versions work on a $900 RX 7900 XTX GPU). It supports 256k context, integrates natively with Claude Code, Cline, and Cursor, and is Apache 2.0 licensed. The model was trained on 800,000 verifiable coding tasks mined from real GitHub PRs — not synthetic benchmarks — which contributes to its strong agentic coding performance. It scores 56.32% func-sec@1 on CWEval (security-focused coding eval), outperforming DeepSeek-V3.2, and is the top recommended local coding model per Latent.Space AINews as of April 2026. Available directly on Ollama. Qwen3-Coder-Next launched in February 2026 but is trending strongly on GitHub today, driven by fresh community benchmarks showing it holding its own against proprietary models on real-world coding tasks. For developers wanting a capable coding agent without API costs or data-sharing concerns, this is currently the best open-weights option.
Open Source Models
Ternary Bonsai
1.58-bit LLMs that run at 82 tok/s on M4 Pro and on your iPhone
75%
Panel ship
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Community
Free
Entry
PrismML's Ternary Bonsai is a family of aggressively quantized language models that take the BitNet concept to its logical extreme. Each weight is constrained to one of three values — {-1, 0, +1} — with a shared FP16 scale factor per 128-weight group. No higher-precision escape hatches, no hybrid layers. The result is a 9x reduction in memory footprint versus standard 16-bit models. The numbers are striking: the 8B model fits in 1.75 GB and hits 82 tokens per second on an M4 Pro. More impressively, it runs at 27 tokens per second on an iPhone 17 Pro Max — fast enough for real-time conversation on-device. The 8B variant scores 75.5 average across standard benchmarks, outperforming many models that are 9-10x larger. The 4B and 1.7B variants push further into mobile-optimized territory. All three models are released under the Apache 2.0 license, available on Hugging Face and GitHub, and integrated into the Locally AI iOS app for immediate on-device deployment. For developers building privacy-sensitive applications or anyone tired of paying cloud inference costs, Ternary Bonsai offers a compelling on-device alternative that doesn't require a beefy GPU.
Reviewer scorecard
“A coding agent that runs locally on a consumer GPU, integrates with Claude Code and Cursor, and outperforms DeepSeek-V3.2 on security-focused coding evals — this is exactly what the ecosystem needed. Training on real GitHub PRs rather than synthetic data shows in the output quality. If you're not using this for local-first coding workflows, you're paying API costs you don't need to.”
“82 tokens per second on M4 Pro in 1.75 GB is a genuinely impressive engineering achievement. For local tooling, code assistants, or any latency-sensitive workload where I don't want cloud round-trips, this hits a sweet spot that larger quantized models miss. Apache 2.0 means I can embed it in commercial apps without legal headaches.”
“56.32% on CWEval is good but not 'beats Claude' good — that framing in the community is overselling it. It's best-in-class for *open weights*, which is a narrower claim. And 'Alibaba open source' carries real enterprise risk: Apache 2.0 today doesn't mean the weights stay available or the license doesn't change. DeepSeek's previous license complications are a useful cautionary tale.”
“A 75.5 benchmark average sounds good until you compare it against 8B models quantized with GGUF Q8 — which score similarly and have years of tooling, community support, and production deployments behind them. The 9x memory savings matter on constrained devices but less so on any machine with 16GB+ RAM. Niche but real use case.”
“The fact that you can run a capable coding agent on $900 of consumer hardware — on an open-weights model with no API dependency — is a structural shift in who has access to AI-assisted development. Open-source coding agents at this capability level make serious software development accessible to the long tail of developers globally, not just those with budget for proprietary APIs.”
“On-device AI at 27 tokens per second on a phone is the inflection point that makes LLMs a platform primitive rather than a cloud service. Once inference is this cheap and fast on commodity hardware, the entire economic model of AI-as-API-call collapses. Ternary quantization is an early signal of where efficiency research is heading.”
“For prototyping and building tools where I don't want my code leaving my machine, this is now my default. The Claude Code integration means I don't have to change my workflow — just swap the backend model. Apache 2.0 means I can actually build products on top of it without legal ambiguity. Strongly recommend.”
“The prospect of running a capable LLM entirely on my iPhone without sending any data to a server is genuinely exciting for creative work with sensitive material. Drafting, editing, and ideation without a cloud subscription or privacy concerns — I'd pay for that, and here it's free.”
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