On 29 May 2026, xAI announced Grok Build 0.1. xAI launched a purpose-built coding model around high output speed and low, symmetric-enough developer pricing.
This release brief was checked against first-party material on 10 August 2026. The date above is the public announcement date, not the date a repository was created or a third-party provider added the model. Where access or weights arrived later, that distinction is recorded below.
Release record
| Field | Verified detail |
|---|---|
| Announcement | 29 May 2026 |
| Availability or weight release | 29 May 2026 as a public beta through named coding platforms |
| Release type | proprietary coding-model public beta |
| Access | hosted partner and API access; no open weights |
| Architecture | an undisclosed coding-specialised Grok model |
| Maximum stated context | the beta context limit documented by xAI and participating providers |
What changed
Grok Build 0.1 marked xAI’s move from a general assistant into the coding-agent runtime market. The announcement focused on more than 100 output tokens per second and pricing of one dollar per million input tokens and two dollars per million output tokens. That makes the initial evaluation question economic: can the beta complete real repository work with fewer retries and less reviewer intervention than slower, more expensive models?
The practical comparison is therefore not simply whether Grok Build 0.1 has the largest headline score. Teams need to ask whether its architecture, access terms, latency, tool behaviour and evaluation setup match the workload they actually intend to run. A model can lead one harness while losing on cost, refusal behaviour, multilingual quality or repeatability in another.
Benchmarks worth retaining
| Evaluation | Reported result | How to read it |
|---|---|---|
| Output speed | more than 100 tokens per second | Vendor-reported beta throughput |
| Input price | $1 per million tokens | Launch pricing before provider overhead |
| Output price | $2 per million tokens | Launch pricing; retries can dominate total task cost |
These are release-time results, not independently reproduced guarantees. xAI did not publish a detailed numerical coding-benchmark table in the launch article, so speed and price should not be mistaken for demonstrated task quality. Scores should remain attached to the disclosed effort setting, agent harness, tool access, timeout, context-management policy and judge model. Moving a number into a procurement sheet without those conditions creates false comparability.
Architecture and access
Grok Build 0.1 is described as an undisclosed coding-specialised Grok model with the beta context limit documented by xAI and participating providers of stated context. Its access position at verification time is hosted partner and API access; no open weights. That wording matters: open weights, source-available weights, an API, a product preview and a research demonstration give adopters very different rights and different levels of reproducibility.
Before deployment, record the exact model identifier or checkpoint, inference stack, quantisation, reasoning setting, region, price schedule and supplier terms. If the release uses a custom licence, read the licence itself rather than relying on the word “open” in launch copy. If it is API-only, preserve the dated documentation and change-notice route because the served snapshot can change without a downloadable artefact.
What an evaluation should test next
For Grok Build 0.1, a credible internal gate should include:
- a frozen set of representative tasks with pass, fail and abstain criteria;
- a matched baseline using the same tools, timeout, prompt budget and reviewer rubric;
- repeated runs to expose variance rather than reporting a single best attempt;
- latency, token use and total task cost alongside task success;
- adversarial, multilingual and long-context cases relevant to the real deployment; and
- rollback evidence showing the previous model can be restored safely.
The wider model change-control guide explains how to keep model, prompt, tool and corpus changes reconstructable. The AI dependency inventory guide covers the release and supplier records needed after deployment.
AIEngine verdict
The release is worth tracking as a low-cost coding beta, but evidence was thin at launch. Teams should demand reproducible repository results and monitor snapshot changes before treating it as a stable production dependency.
This is a launch assessment, not a certification. Benchmark leadership is useful evidence of where to test; it is not authorization to place the model in a high-impact workflow without domain evaluation, security review and an accountable owner.
Primary sources
Image provenance
Hero image: xAI official release artwork. The locally served WebP is a crop of the first-party release or model-card asset recorded in the repository provenance manifest.
