we found something interesting and wanted to share it with this community.

we wanted to know how google's gemma 4 e2b-it — 2 billion parameters, bfloat16, apache 2.0 — stacks up against gpt-3.5 turbo. not in vibes. on the same test. mt-bench: 80 questions, 160 turns, graded 1-10 — what the field used to grade gpt-3.5 turbo, gpt-4, and every major model of the last three years. we ran gemma through all of it on a cpu. 169-line python wrapper. no fine-tuning, no chain-of-thought, no tool use.

gpt-3.5 turbo scored 7.94. gemma scored ~8.0. 87x fewer parameters, on a cpu — the kind already in your laptop.

but the score isn't what we want to talk about. what's interesting is what we found when we read the tape.

we graded all 160 turns by hand. (when we used ai graders on the coding questions, they scored responses as gpt-4o-level.) the failures aren't random. they're specific, nameable patterns at concrete moments in generation. seven classes.

cleanest example: benjamin buys 5 books at $20, 3 at $30, 2 at $45. total is $280. the model writes "$245" first, then shows its work — 100 + 90 + 90 = 280 — and self-corrects. the math was right. the output token fired before the computation finished. we saw this on three separate math questions — not a fluke, a pattern.

the fix: we gave it a calculator. model writes a python expression, subprocess evaluates it, result comes back deterministic. ~80 lines. arithmetic errors gone. six of seven classes follow the same shape — capability is there, commit flinches, classical tool catches the flinch. z3 for logic, regex for structural drift, ~60 lines each. projected score with guardrails: ~8.2. the seventh is a genuine knowledge gap we documented as a limitation.

one model, one benchmark, one weekend. but it points at something underexplored.

this model is natively multimodal — text, images, audio in one set of weights. quantized to Q4_K_M it's 1.3GB. google co-optimized it with arm and qualcomm for mobile silicon. what runs it now:

phones: iphone 14 pro+ (A16), mid-range android 2023+ with 6GB+ ram

tablets: ipads m-series, galaxy tab s8+, pixel tablet — anything 6GB+

single-board: raspberry pi

laptops: anything from the last 5-7 years, 8GB+ ram

edge/cloud: cloudflare containers, $5/month — scales to zero, wakes on request

google says e2b is the foundation for gemini nano 4, already on 140 million android devices. the same model that matched gpt-3.5 turbo. on phones in people's pockets. think about what that means: a pi in a conference room listening to meetings, extracting action items with sentiment, saving notes locally — no cloud, no data leaving the building. an old thinkpad routing emails. a mini-pc running overnight batch jobs on docs that can't leave the network. a phone doing translation offline. google designed e2b for edge from the start — per-layer embeddings, hybrid sliding-window/global attention to keep memory low. if a model designed for phones scores higher than turbo on the field's standard benchmark, cpu-first model design is a real direction, not a compromise.

the gpu isn't the enemy. it's a premium tool. what we're questioning is whether it should be the default — because what we observed looks more like a software engineering problem than a compute problem. cs already has years of tools that map onto these failure modes. the models may have just gotten good enough to use them. the article has everything: every score, every error class with tape examples, every fix, the full benchmark harness with all 80 questions, and the complete telegram bot code. run it yourself, swap in a different model, or just talk to the live bot — raw model, no fixes, warts and all.

we don't know how far this extends beyond mt-bench or whether the "correct reasoning, wrong commit" pattern has a name. we're sharing because we think more people should be looking at it. everything is open. the code is in the article. tear it apart.