Best Ollama Model for Coding in 2026: Picked by VRAM (and Reasoning Tokens)

The best Ollama model for coding depends mostly on your GPU — VRAM decides which open coding models you can actually run locally. This guide picks the strongest coding models for each VRAM tier, works through the quantization math that lets you punch above your hardware, and adds something no other guide on this query has: our own measured scores for seven of these open-weight models in their API-hosted form. We have never run a model through Ollama, so we publish no local speed numbers. What does carry over is correctness and verbosity — and reasoning tokens per task, which is the single measured number that predicts how slow a model will feel on your own hardware.

The short answer

The best Ollama coding model is the largest capable coder your VRAM can run — usually a Qwen3 Coder variant. DeepSeek-Coder and Codestral are strong alternatives. Match the model size to your GPU, run it at a sensible quantization, and keep a hosted frontier model on hand for the hardest tasks.

The part most guides skip: two models that fit the same VRAM budget and score the same can still feel completely different to use, because one of them thinks out loud before answering and the other does not. That difference is measurable, we measured it, and it is the most useful thing on this page. Jump to reasoning tokens if that is why you are here.

Best model by VRAM

VRAM is the hard constraint. Everything else is preference. These tiers are the standard rule of thumb for Q4-quantized weights plus KV cache headroom — they are not a benchmark result of ours.

If you are still deciding whether local is the right shape at all, running LLMs locally in 2026 covers the runner choice and the hardware floor, and how to run DeepSeek locally walks one model end to end.

What we measured, and what Ollama changes

We have never run a model locally through Ollama. Our harness calls API-hosted endpoints. So we cannot publish first-party numbers about Ollama performance and we are not going to imply we can — local speed is a function of your GPU, your quantization, your context length and your runner, none of which we tested.

What we can contribute is this: several of the open-weight models people actually run under Ollama have been through our benchmark in their API-hosted form. That tells you about the model — whether its code passes hidden assertions, and how many tokens it burns getting there. Those properties belong to the weights and travel with them. Cost and latency do not: the dollars below are hosted list rates, and the seconds below are somebody else's datacentre GPU.

Read the columns in two groups. Score and reasoning tokens describe the weights, so they mean something for a local run. Measured cost and mean latency describe a hosted endpoint on a particular day and mean nothing for your laptop — the column headers say so, and we kept them only because they are the evidence that the run happened at all. Full method: our methodology. Full run: the coding cost benchmark.

One more honesty note about model identity. What we ran are the hosted checkpoints of these model families, not a specific Ollama tag at a specific quantization. A Q4 quant of the same weights is a different artefact — usually close, sometimes not — so treat the score column as an upper bound on what the quantized local version will do, never as a promise.

Reasoning tokens: the number that transfers

Here is the rule this page exists to give you. On local hardware, every reasoning token is wall-clock time you personally pay for. A model that emits 559 reasoning tokens before it starts answering is generating 559 tokens on your GPU, at your tokens-per-second, before the first character of code appears. On a hosted endpoint that is somebody else's problem and a few cents. On a laptop it is the whole experience.

This is why Qwen3 Coder Next should feel responsive on a local box, and why the reasoning-heavy models are the ones that read as hung — a prediction from our hosted token counts, not a local measurement of ours. It emitted 0 reasoning tokens per task across all nine tasks in our run and still scored 9/9. It answers directly. There is nothing to wait through.

The spread is the finding: 0 to 732 on the same nine problems. Six of these seven models scored 9/9 anyway. The model at the bottom of the chart, DeepSeek V4-Pro, emitted the most reasoning tokens of the seven and was the only one that missed a task (parse_csv_line). More thinking did not buy correctness here. It would, however, buy you a long stare at a blinking cursor on a local box.

So the rule, stated plainly: prefer a model that scored well and emits few reasoning tokens. Every competing guide on this query ranks local coders by parameter count or by vibes. Parameter count tells you whether the model fits in VRAM. Reasoning-token count tells you whether you will enjoy using it once it does. They are different questions and only one of them is usually answered.

Two practical corollaries. First, a reasoning-heavy model has variable latency — the token count moves with how awkward the prompt is, not how long it is — so a local reasoning model is unpredictable in a way a direct-answering one is not. Second, reasoning tokens are worst exactly where local shines: inline autocomplete and quick edits, where a human is watching. For batch work overnight, they matter far less. If you want either of the big open models on this page looked at on its own, our GLM-5.2 review and the Nemotron 3 Ultra review go task by task.

Open coders compared

The last column is the useful one. Where we have no number we say so rather than filling the cell with a leaderboard someone else produced. For the wider open-weights field, see best open source LLM in 2026 and open weights vs open source, which explains why several of these licences are not what the label suggests.

The quantization tradeoff

Quantization (e.g. Q4_K_M) shrinks a model's memory footprint by storing weights at lower precision. It costs a little quality but lets you run a much larger, more capable coding model on the same GPU. The key insight: locally, a larger model at Q4 usually beats a smaller model at full precision — so quantize down to fit the biggest capable coder your VRAM allows, rather than running a small model at FP16. Q4_K_M is a common coding sweet spot; only drop to lower bit-depths if VRAM forces it, since quality degrades faster below Q4.

This interacts with the reasoning-token point in a way worth stating. Quantization makes each token cheaper to produce; it does not reduce how many tokens the model wants to produce. A reasoning-heavy model quantized to Q4 is still a reasoning-heavy model — you have made the per-token cost smaller and left the token count exactly where it was. Picking a low-reasoning model is the bigger lever, and it is free.

We did not benchmark any quantized weights, so nothing in our data speaks to how much quality Q4 costs. That question is open on this page.

The top picks

When hosted wins (the break-even)

Local is great for autocomplete, small refactors, and private or offline work. But run the numbers before assuming it is cheaper:

• Self-host cost: an always-on cloud GPU capable of serving a mid-size coder lists at roughly $1-2/hour, which is $700-1,500/month if you leave it running, plus setup and ops. Those are published instance rates, not something we measured.
• Hosted cost, from our own run: Qwen3 Coder Next measured $0.10 per 1,000 tasks at 2026-07-17 list prices. A thousand tasks a day of roughly that size projects to about $37 a year — a projection from a measured per-task figure, not a bill anyone sent us. Even GLM-5.2 at $1.99 per 1,000 tasks projects to roughly $730 a year on the same volume.
• Break-even: one month of that GPU costs more than a year of the cheapest hosted open coder at our measured rate and that volume. The GPU only pays off at very high, sustained throughput — or when the point was never cost.

Which is the honest framing: run local for privacy, offline work, and freedom from per-token billing, not because it is cheaper at small scale. Those are good reasons. Cost usually is not one, and the guides that claim otherwise rarely price the idle hours. The cheapest LLM APIs in 2026 has the hosted end of that comparison, and the cost calculator does the arithmetic on your own token mix.

The pragmatic setup: local for routine and private work, a hosted API for the hard cases — and the same open models you run in Ollama are available hosted, so prototyping local and scaling hosted needs no model change. For the cheap hosted tier specifically, see the cheap coding model roundup.

What we did not measure

Nothing local, at all. No Ollama run, no llama.cpp run, no quantized weights, no tokens-per-second on any consumer GPU. If a local speed number attributed to us ever appears on this site, it is an error.

The harness itself is narrow. Nine executed Python tasks. The model gets a function signature and a prose spec and never sees the assertions; the returned code runs against hidden assertions in an isolated python3 -I subprocess with a 12-second timeout. Temperature 0, max_tokens 4000, one scored attempt per task, retrying only on an API error. It does not test long context, multi-file refactoring, agentic or multi-turn tool use, non-Python languages, vision, or translation.

Cost is derived, not invoiced. It is the token counts the API reported multiplied by that model's list price on the date shown in the table. List prices move — 49 of roughly 396 catalogue models changed price in the twelve days to 2026-07-29 — so recompute against current rates before acting on any dollar figure here.

Where the numbers come from. Our core sweep was 13 models run in one sitting; ten of those 13 scored 9/9. Others have since run on the same harness under the same settings, bringing the total to 23 models, 20 of which scored 9/9. Qwen3 Coder Next, GLM-5.2 and Nemotron 3 Ultra were all in the original core 13. Calls go through OpenRouter's OpenAI-compatible endpoint, deliberately not through the DataLLM Lab gateway — nothing here depends on our infrastructure and you do not have to be our customer to reproduce it.

Not tested, and never claimed as ours: any locally-run or Ollama deployment, Codestral, any Llama model, Claude Fable 5, GPT-5 nano, Grok 3, Grok 4, Grok 4.20, grok-build-0.1, VibeThinker, and any Gemini other than 3.6 Flash and 3.1 Pro.

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