Published 2026-05-17

Apple silicon costs more than OpenRouter.

At ~50-100 watts under load, and ~$0.20 per kWh, my M5 MacbookPro will cost a few cents per hour. Accelerated depreciation (if any) from shortening the lifespan of the device will be more expensive than the electricity. At a few tens of tokens per second this works out to ammortized costs of ~$1.50 per million tokens. Openrouter for comparable models is 1/3rd the price and ~2x the speed.

Electricity

In Northern Virginia my last electricity bill worked out to $0.18 per kilowatt hour. Let's round up to $0.20 per kWh.

EIA has average residential costs for 2025 at $0.1730 per kWh in the US.
https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=table_5_03

At ~50-100 watts and $0.18/kWh that's $0.009 or $0.018 per hour. $0.02 per hour. $0.48 cents per day for the electricity to be running inference at 100%.

Hardware

A 14 inch MBP with M5 Max and 64 gigs of ram is currently listed as $4299 on the apple website. 128 gigs will cost you more but 64 gigs should run a model like Gemma 4 31b, which is almost anthropic sonnet levels of performance.

For cost allocation, let's consider that this hardware will last 3, 5, or 10 years. The cost per year is $1433, $860, or $430 respectively.

The hourly cost over 3, 5, and 10 years is thus:

• $0.16358
• $0.09815
• $0.04908

Depending on useful lifespan, I think 5 years is a reasonable estimate for normal use. 7 or 10 is very plausible. At maxed out inference 3 years may be a reasonable estimate as well.

Tokenomics

The big question is how many tokens per hour can you get out of a local model. My M5 Max testing seems to be in the 10-40 tokens per second range for a serious model like Gemma4:31b. At 10 tokens per second that's 36000 tokens per hour.

36000 tokens per hour across our 3-10 year lifespan at $0.18 per kwh gives a price per million tokens of $1.61 to $4.79 on the high end.

At 40 tokens per second that's 144000 tokens per hour which gets you to $0.40 to $1.20 per million tokens.

For apple silicon, the hardware cost dominates.

OpenRouter

OpenRouter has Gemma4 31b at ~38-50 cents per million tokens. This means that on the optimistic side (50 watts, 40 tokens per second, and 10 years) the pro max is as cheap as openrouter. On the pessimistic side (100 watts and 3 years at 10 tokens per second) the pro max is 10x the cost. I think ~3x the cost per million tokens is likely the right number for local inference on the pro max from an accounting perspective.

Conclusion

Speed of inference is the biggest factor here though for most cases. Local inference is slower than cloud inference. Some of the gemma 4 providers on openrouter get up to 60-70 tokens per second, which is 3-7 times faster than what I'm seeing with the pro max (~10-20 tokens per second). For a human employee with a work laptop, their salary costs are going to be ~1000x the cost of the tokens they can generate locally. Throwing money at anthropic makes more sense in this context.

It's still wild that a consumer device can run models that are close to anthropic sonnet levels of performance.

Jankmarking: Janky Benchmarking

Local LLM performance frontier: Ollama with M5 Pro

Are janky benchmarks useful?

I put together some evals for local benchmarking. I'm mostly interested in approximately measuring performance versus speed because I don't like waiting while my macbook imitiates a jet turbine in noise and temperature and want to get good enough answers locally. For anything bigger the cloud is still the move. The problem is my benchmarks are --- to use a human em-dash and quote the kids these days "hella jank".

my janky benchmark questions:

• hello: Say hello in one sentence.
• haiku: Write a haiku about autumn leaves.
• reasoning: If a train travels 60 mph for 2.5 hours, how far does it go? Show your work.
• summarize: Summarize the following in two sentences: The Apollo program was a NASA spaceflight program that successfully landed humans on the Moon from 1969 to 1972, using the Saturn V rocket and Command/Service Module.
• code_simple: Write a Python function that returns the nth Fibonacci number.
• code_complex: Implement a thread-safe LRU cache in Python with O(1) get and put. Include type hints and a brief docstring.

These aren't very good. They're half AI generated. The summarization one is a single sentence and is actually backwards asking for a summary longer than the input sentence... (it used to be longer but I think it got lost at some point when I copy and pasted it. Never made it into version control... )

And yet, these are directionally correct. The better models cluster together scorewise.

For serious work, make sure your bench marks aren't jankmarks.

Published 2026-05-07

OpenCode:

Claude code with non-anthropic models feels limited. Conveniently, we can also use OpenCode!

ollama launch opencode

OpenCode is like Claude Code but bring your own model. Overall it's very comparable, but is less polished in some ways while feeling more solid in others.

Kilocode:

Kilocode (kilo.ai) is an agentic coding platform. It's now primarily powered by a fork of OpenCode. Their VScode/codium extension is very nice, and offers one of the my favorite views of the context window:

Hooking up a local model to the Kilocode vscode extension is straightforwad. Just give it your local server port and you're good to go.

Kilocode is also nice because they have support for a wide range of model providers as well as their own model provider platform, so it's seemless to switch between local, open, and proprietary models.

Overall

Overall local models for coding are still too slow to be practical on regular hardware, but it's nice to have as a capability if the internet goes down, and it's still magical to be able to tell me computer to program itself.

Published 2026-04-27

You can use ollama as the backend for claude code!

ollama launch claude --model

This allows you to use claude code with local models. I'm writing this from an airplane with no internet connection.

Overall model comparisons

Gemma4:e2b did not finish any tasks despite being blazing fast at over 100 tokens per second.

qwen3-coder-next:q4_K_M actually did reasonably well. Felt a bit worse than haiku quality but notably slower. Took around half an hour to fill up 75k of context, which is about 40 tokens per second while taking 50-60gb of memory.

qwen3.6:35b was also fairly reasonable. Did an adaquate job writing a small local data processing job, but was also fairly slow.

Gemma4:31b felt the most 'claude-like' in claude code, but was also fairly slow and occasionally required some jostling and interruption.

Overall

I don't seriously recommend local agentic coding with LLMs. You need some serious hardware to run decent models and it's still slow. It's a nice capability to have locally, but it probably isn't better than coding by hand. Still very cool to have a computer that can program itself though, and amazing that a consumer device can locally run models and software that matches the original gpt-3 era ChatGPT style experience.

An analysis of 85 years of daily weather data from Reagan National Airport.