xAI, released Apr 16, 2026
Grok Build 0.1price, context, benchmarks and release details
- Input, per 1M tokens
- $1.00models.devFirst-party hosted API; MIT models.dev transcription. Provider documentation: https://docs.x.ai/docs/models. Exact canonical endpoint; lowest short-context Standard USD token tier; cache/batch discounts excluded. Deprecated endpoints excluded; xai/grok-build-0.1
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Output, per 1M tokens
- $2.00models.devFirst-party hosted API; MIT models.dev transcription. Provider documentation: https://docs.x.ai/docs/models. Exact canonical endpoint; lowest short-context Standard USD token tier; cache/batch discounts excluded. Deprecated endpoints excluded; xai/grok-build-0.1
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Context window
- 256Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Max output
- 256Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Apr 16, 2026models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
How the score breaks down
Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 06:15 UTC.
Around it on the leaderboard
- 1 Claude Fable 5.1 80.2
- 2 Claude Opus 5.5 78.4
- 3 GPT-6 Astra 77.5
- 4 Claude Fable 5 76.8
- 5 Claude Opus 5 74.9
Benchmark results
16 benchmarks, 16 resultsEach row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.
Open source ↗ 84.0
Open source ↗ 69.7
Open source ↗ 70.0
Open source ↗ 98.0
Open source ↗ 69.2
Open source ↗ 68.3
Open source ↗ 66.0
Open source ↗ 95.1
Open source ↗ 66.0
Open source ↗ 86.6
Open source ↗ 62.6
Open source ↗ 67.4
Open source ↗ 63.4
Open source ↗ 59.1
Open source ↗ 45.0
Open source ↗ 33.3
Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.
Details and sources
- Open weights
- Nomodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- text, image, pdfmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 13% of expected source weight
Reported (1)
- LiveBenchOct 8, 2026
Awaiting (6)
- ARC Prize13% of weight
- Epoch AI Benchmarking25% of weight
- Humanity’s Last Exam13% of weight
- Official model cards via models.dev4% of weight
- LMArena / Arena25% of weight
- Terminal-Bench6% of weight
Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.