Grok 4.20
xAI · Proprietary · rank 27 · bench-align-v5
Canonical idgrok-4-20-beta
Access typeProprietary
Blended $/1M$3.0075% input / 25% output
Capability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
Runtime service evidence
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: Observed
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | 21.21 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 104 tok/s | 2026-09-17 | 2026-09-17 |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Endpoint and price matrix
Every published price component, including cache reads and writes.
| Component | USD | Evidence |
|---|---|---|
| Input / 1M tokens | $2.00 | |
| Output / 1M tokens | $6.00 | |
| Cache read / 1M tokens | Unavailable | |
| Cache write / 1M tokens | Unavailable | |
| Blended / 1M (75% input / 25% output) | $3.00 |
Self-hosted listingOfficial xAI docs pricing for grok-4.20-0309-reasoning. xAI's current model docs present this row as Grok 4.20. A non-reasoning variant also exists at the same token price.The rates above are a hosted price matched from another provider, not a first-party list price.
Workload-aware monthly cost example
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
Benchmark record
24 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA-D GPQA Diamond | 88.5 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| HealthBench Hard HealthBench Hard | 20.3 | 1,000 health prompts | Advanced health reasoning | Muse Spark Eval Methodology |
| MedXpertQA (Text) MedXpertQA Text | 50.2 | 2,450 medical multiple-choice questions | Professional medical knowledge | Muse Spark Eval Methodology |
| HLE w/o tools Humanity's Last Exam without tools | 31.6 | Expert-level questions | Frontier expert level | Introducing GPT-5.4 mini and nano |
| GPQA Diamond (Vals) GPQA Diamond, Vals AI run | 88.6 | Graduate-level science questions | Expert reasoning | Vals AI GPQA Diamond, Vals AI run leaderboard |
| MMLU-Pro (Vals) MMLU-Pro, Vals AI run | 86.3 | Academic multiple-choice questions | Broad academic knowledge | Vals AI MMLU-Pro, Vals AI run leaderboard |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| SWE-bench Verified Software Engineering Benchmark Verified | 76.7 | 500 verified issues | Professional software engineering | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? |
| LiveCodeBench Pro LiveCodeBench Pro | 74.2 | Quarter-specific contest programming sets | High-end contest programming | LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? |
| SWE-bench Pro SWE-bench Pro | 51.8 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| Vibe Code Bench Vibe Code Bench v1.1 | 4.06 | End-to-end web application builds | End-to-end software delivery | Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development |
| LiveCodeBench (Vals) LiveCodeBench, Vals AI run | 84.3 | Competitive programming problems (easy, medium, hard) | Frontier coding | Vals AI LiveCodeBench, Vals AI run leaderboard |
| SWE-bench (Vals) SWE-bench, Vals AI run | 72.2 | Real repository issues by human time bucket | Frontier coding agents | Vals AI SWE-bench, Vals AI run leaderboard |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| ARC-AGI-2 Abstraction and Reasoning Corpus for AGI v2 | 53.3 | Visual pattern completion and abstract reasoning | Expert-level — hardest public reasoning benchmark | ARC-AGI-2: A Harder General Intelligence Benchmark |
| ARC-AGI-3 Abstraction and Reasoning Corpus for AGI v3 | 0.09 | Interactive game-like tasks with hidden rules | Frontier agentic reasoning | ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 47.1 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| Gert Labs Gert Labs Composite Game Benchmark | 38.36 | Novel game environments | Agentic coding and decision-making | Gert Labs rankings |
| DeepSearchQA DeepSearchQA | 62.8 | Agentic browsing and list-answer questions | Agentic web research | Muse Spark Eval Methodology |
| Terminal-Bench 2.1 (Vals) Terminal-Bench 2.1, Vals AI run | 44.2 | Difficult terminal tasks | Frontier agentic | Vals AI Terminal-Bench 2.1, Vals AI run leaderboard |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMMU-Pro Massive Multi-discipline Multimodal Understanding Pro | 75.2 | Multimodal academic reasoning | Frontier multimodal | MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark |
| Design Arena Website Design Arena Website Elo | 1242 | Website generation comparisons | Design and website generation | OpenRouter Grok 4.3 benchmarks |
| ERQA ERQA | 54.1 | Evidence-based visual QA | Grounded multimodal reasoning | Qwen3.6 launch benchmarks |
| MedXpertQA (MM) MedXpertQA Multimodal | 65.8 | 2,000 multimodal medical questions | Clinical multimodal reasoning | Muse Spark Eval Methodology |
| SimpleVQA SimpleVQA | 57.4 | Visual QA tasks | General visual understanding | GLM-5V-Turbo |
| CharXiv CharXiv Reasoning | 60.9 | Scientific chart reasoning | Scientific visualization reasoning | CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs |
Lifecycle and limitations log
Lifecycle events the source associates with this model, plus what this profile does not claim.
No lifecycle event references this model.
That is not evidence the model has no lifecycle plan — only that this source published none.
What this profile does not claimValues are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned — the attempt timestamp is in each badge. Last attempted fetch for this model’s score: 2026-09-17 17:17 UTC.