The Hitchhiker's Guide to
Open-Weight Models

Kimi K3 is the sort of model that makes you reconsider your life choices, specifically the one where you thought you could run it on your desktop. At 2.8 trillion parameters, it's larger than some small countries' GDP. It activates 50B per token across 896 experts, which is a bit like having 896 specialists in a room and only letting 16 of them speak at a time — oddly efficient, deeply antisocial.

It tops the open-weight leaderboard for coding (76.8% SWE-Bench Verified) and reasoning (93.5% GPQA Diamond). It's the model you'd want if you were trying to compute the Ultimate Question of Life, the Universe, and Everything, assuming you had the hardware budget of a mid-sized nation.

DeepSeek V4 Pro is what happens when a team looks at the cost of frontier APIs and says "we can do that for free, sort of, if you buy enough GPUs." At 1.6T parameters with only 49B active, it uses DeepSeek Sparse Attention and token-wise compression to deliver 80.6% on SWE-Bench Verified and 90.1% on GPQA Diamond — both within spitting distance of GPT-5 territory.

The model ships under MIT license, which is the licensing equivalent of someone handing you the keys and saying "do whatever you want, we're not looking." DeepSeek's official API moved to peak/off-peak pricing in August 2026, which is the AI equivalent of your electricity provider charging you more at dinner time.

GLM-5.2 is the model equivalent of the Heart of Gold's Infinite Improbability Drive — it does things that shouldn't be possible at speeds that don't make sense. At 347 tokens per second, it generates text faster than you can read it, while still scoring 91.2% on GPQA Diamond and leading Terminal-Bench 2.1 at 81.0. It's the model you want when you need the answer yesterday.

The full parameter count is not publicly disclosed (which is delightfully mysterious), but we know it has a 1M context window and the API costs are genuinely absurd: $0.95/$3 per million tokens. That's less than a Dublin coffee per million tokens of output.

Llama 4 Maverick is the workhorse of the open-weight galaxy. It's the model that most enterprises settle on because it's from Meta, it's good enough, and the community license lets you use it commercially as long as you don't have 700 million monthly active users (if you do, you have bigger problems than licensing).

With 400B total parameters and only 17B active per token across 128 experts, it's surprisingly efficient for its size. It beat GPT-4o and Gemini 2.0 Flash on multiple benchmarks when it launched — though there was some controversy about Meta's evaluation methodology, which is the AI equivalent of "the dog ate my benchmark."

Mistral Large 3 is what happens when a French AI lab decides to build a frontier-class model and, in a move that shocked absolutely everyone, releases it under a proper Apache 2.0 license. No field-of-use restrictions, no 700 million user cap, no "we'll change the terms later" clause. Just a clean, permissive license. The Vogons are baffled.

At 675B total with 41B active, it's a big MoE model with a 256K context window. It leads on multilingual tasks outside English and Chinese, posts ~92% on HumanEval, and the fact it's made in France means EU data residency is trivially easy — which matters if you're a bank and GDPR is your religion.

Qwen 3.5 is what Alibaba built after looking at the entire AI landscape and deciding the best strategy was to release a model in every conceivable size, all under Apache 2.0, and let the market sort it out. The flagship 397B-A17B has 397B total parameters with only 17B active, native multimodal (text + vision), and supports 201 languages — which is roughly 199 more languages than most people will ever need.

It uses a hybrid Gated Delta Networks + sparse MoE architecture, which is the kind of name that sounds like it was generated by one of these models trying to sound impressive. It works, though — Qwen consistently leads on multilingual and long-context retrieval tasks.

Qwen3-Coder is what happens when you take 7.5 trillion tokens of training data, make 70% of it code, and ask "what if a model could actually do software engineering?" The answer is: it can, alarmingly well. It set SOTA among open models on agentic coding benchmarks and handles 256K context natively (up to 1M with extrapolation).

At 480B total with 35B active, it's large enough to be serious but small enough that you don't need to sell a vital organ to run it. Apache 2.0 means you can build a product around it without a licensing conversation that makes your lawyer sweat.

DeepSeek V4 Flash is what happens when you take the V4 Pro, make it smaller, and decide that the API price should be lower than the cost of the electricity to run a desktop GPU. At 284B total with only 13B active, it's the model you reach for when you want frontier-adjacent quality but your finance department has opinions.

The API costs $0.14/$0.28 per million tokens. That's not a typo. Fourteen cents per million input tokens. You could process the entire works of Shakespeare for less than the price of a pint in Dublin.

Mistral Medium 3.5 is a 128B dense model that merges instruction-following, reasoning, and coding into a single set of weights. No MoE, no experts, no routing — just a straightforward dense model that does what it says on the tin. Released under a modified MIT license, which is nearly as permissive as it gets.

It's the model for people who want something genuinely capable without the complexity of MoE routing, quantisation strategies, and expert distribution tuning. Sometimes simple is good. Sometimes simple is all you need.

Llama 4 Scout has a 10 million token context window. Ten. Million. Tokens. That's roughly 7.5 million words, or about 75 copies of War and Peace, or the entire collected works of Shakespeare plus a comfortable margin of your tax returns. The Guide is not entirely sure what you'd put in a 10M context window, but it's nice to know you could.

At 109B total with 17B active across 16 experts, Scout is the model that fits on a single H100 80GB at INT4 quantisation. It's natively multimodal, which means it can look at images while pretending to understand them — much like a cat watching television.

Mistral Small 4 is what Mistral calls a model that does reasoning, multimodal understanding, and agentic coding — all in one, all under Apache 2.0, all in 119B total parameters with only 6.5B active. Six point five billion active parameters. That's less than a phone keyboard's autocomplete, and it can write code.

With configurable reasoning effort, it's the model that lets you choose how hard it thinks before answering — a feature most humans could benefit from. 256K context, multimodal, and it runs on a single GPU. This is the model the Guide recommends for people who want capability without infrastructure headaches.

Gemma 3 27B is Google's "we can do open too" model, built from the same research as Gemini but packed into 27 billion dense parameters that fit on a single consumer GPU. It outperformed Llama 3.1 405B in human preference evaluations — which is a bit like a hatchback beating a lorry in a race. Supports 140+ languages and 128K context.

It's the model the Guide recommends for individuals, researchers, and small teams who want something genuinely capable without needing a data centre, a cooling system, or a second mortgage. The Gemma license is slightly restrictive compared to Apache 2.0, but it's permissive enough for most commercial use.

DeepSeek R1 is the reasoning model that showed the world you don't need to be OpenAI to build something that thinks out loud. Trained with reinforcement learning specifically for reasoning tasks, it shows its chain-of-thought process before answering — like a student who got full marks for showing working, except the student is 671 billion parameters.

The distilled variants are where it gets interesting for normal humans: R1-Distill-Qwen-32B fits on a single consumer GPU and achieves ~90% on AIME. The full R1 shares V3's 671B MoE architecture. MIT licensed, because DeepSeek apparently believes in giving things away.

Phi-4 is Microsoft's proof that with enough synthetic data and careful training, a 14B model can punch well above its weight. It scores 80.4 on MMLU and 82.6 on HumanEval — numbers that would have been frontier-class eighteen months ago, now available in a model that runs on a laptop.

MIT licensed, 14B dense parameters, 16K context (short, but sufficient for most tasks). This is the model you run locally when you don't have a GPU at all — it works on CPU, slowly, like a marathon runner with asthma, but it gets there.

Qwen 3.5-35B-A3B is the model that makes you question why you'd ever pay for an API. 35B total parameters, 3B active. Three billion active parameters — that's less than some smartphone keyboards use for autocorrect, and it's genuinely useful. Apache 2.0 licensed, 262K context, and it runs on an RTX 4090.

This is the sweet spot of the open-weight galaxy: small enough to run at home, smart enough to be useful, cheap enough to be free. The Guide considers this the model for anyone who wants to try open-weight AI without spending more than the cost of a PlayStation.

Nemotron 3 Nano is NVIDIA's entry — the GPU company building its own models to run on its GPUs. It's a bit like a pub brewing its own beer: vertically integrated, slightly surprising, and not bad at all. 30B parameters, 1M context window, and optimised specifically for NVIDIA hardware.

Scores 78.3 on MMLU and 73.0 on GPQA. The NVIDIA Open Model license is permissive but not Apache 2.0 — read it before shipping. This model exists because NVIDIA wants you running models on their silicon, and this one is tuned to do exactly that efficiently.