whatmodel.sh / Grok 4.20 (0309 reasoning)

Grok 4.20 (0309 reasoning)

+ contribute

Grok 4.20 (0309 reasoning) is a closed-weights language model from SpaceXAI (xAI), released April 2026.

71.6±0.8#69 of 719 LLMsfrom 15 benchmark results across 6 sources

$ how it compares

The LLMs ranked either side of Grok 4.20 (0309 reasoning), out of 828 with a score, then more from SpaceXAI (xAI).

#	model	score	where	RAM	released
72	Kimi K2.7 Code Moonshot AI	72±1	api $1.71/M +local	~359GB	2026-06
73	DeepSeek V4 Flash (Preview, Apr 2026) DeepSeek	72±1	local	~103GB	2026-04
74	Grok 4.20 (0309 reasoning) SpaceXAI (xAI)	72±1	api $1.56/M		2026-04
75	Solar Open 2 (250B-A15B) Upstage	72±1	local	~527GB	2026-07
76	North Small Translate 1.0 Cohere	71±6	api price unknown		2026-08
	more from SpaceXAI (xAI)
30	Grok 4.6 SpaceXAI (xAI)	77±1	api $3.00/M		2026-08
42	Grok 4.5 SpaceXAI (xAI)	75±1	api $3.00/M		2026-07
47	Grok 4.7 SpaceXAI (xAI)	75±1	api $3.00/M		2026-09

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #106 of 300 * · 3.8% of its score · 1 test

Trails SmolLM3 3B, Nova 2 Pro (Preview), Mistral Medium 3.5 (128B) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
τ³-bench banking_knowledge (v1.0.1 grading)	18.0% ✎	▃▃▂▃·▆▃▂█▅▅▂ beats 5/25	very hard · avg 4.1%	▇▇ 3.8%	at its level

coding▁▁▁▃▆██▇▆▄▃▁ #99 of 458 * · 10% of its score · 1 test

Trails Llama 3.1 Nemotron Ultra 253B v1, Nemotron Cascade 2 30B-A3B, MiniMax-M2.1 here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Arena WebDev (Code Arena)	1374.1 ✎	▁▂▄▃▅█▄▇▆▄▁▁ beats 37/119	avg 981	▇▇▇▇ 10%	sharp

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #32 of 393 * · 5.8% of its score · 1 test

Beats Claude Sonnet 5.5, GPT-6 Sol, GPT-5.6 Terra here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
LMArena (text)	1471.5 ✎	▁▁▁▂▃▃▃▆▆▇█▃ beats 343/379	avg 1268	▇▇ 5.8%	blunt

math▁▂▃▅▆▅▄█▄▁▁▁ #53 of 398 · 45% of its score · 3 tests

Beats LongCat 2.0 (1.6T-A48B), Qwen3.6 Max Preview, Claude Sonnet 4.6 here, though they rank higher overall. Trails INTELLECT-3, A.X K2 (688B-A33B) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
FrontierMath tiers 1–3 v2	44.9% ✎	▂··▁▁▃▄▄█▅▃▃ beats 30/82	very hard · avg 1.2%	▇▇▇▇▇▇▇▇▇▇▇▇ 29%	sharp · at its level
FrontierMath tier 4 v2	17.1% ✎	▁▂▁▂█▅▂▂▁▁▁▁ beats 18/63	very hard · avg 0.1%	▇▇▇▇▇▇ 14%	sharp
OTIS mock AIME 2024–2025	92.2% ✎	▃▃▄▃▃▃▇▇█▃▂▅ beats 152/193	very hard · avg 8.5%	▇ 2.1%	sharp · noisy

vision & documents▁▁▃▂▁▃▄▂▆▇█▄ #41 of 147 * · 6.0% of its score · 2 tests

Beats MiMo-V2.6-Pro, MiMo-V2.6-Flash (309B-A15B), DeepSeek V4 Flash Vision (Exp) here, though they rank higher overall. Trails Qwen3.6 Flash, Gemini 3.5 Flash-Lite, Qwen3.6 Plus here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Arena vision (style controlled)	1255.4 ✎	▁▁▂▂▂▂▄▅▅▆██ beats 106/145	avg 1068	▇▇ 4.0%	blunt
Arena document (style controlled)	1439.4 ✎	▁▁▃▃▄▃▇█▅▇▄▅ beats 8/37	avg 1335	▇ 1.9%	blunt

other tests7 not in a task · 29% of its score

test	result	vs the others in its run	difficulty	counts	why
GPQA Diamond	89.3% ✎	▁▁▁▁▂▄▆▄▅▇█▅ beats 167/208	hard · avg 40%	▇▇▇ 6.3%	sharp
ARC-AGI-2 public evaluation set (ARC Prize model_baseline, pass@2)	63.6% ✎	█··▂▅▁▃▁▃·▃▃ beats 21/24	very hard · avg 0.3%	▇▇ 5.8%	sharp · at its level · noisy
ARC-AGI-1 public evaluation set (ARC Prize model_baseline, pass@2)	95.5% ✎	▁·▇▃▁·█▇▁·▇▃ beats 23/27	very hard · avg 4.1%	▇▇ 3.8%	sharp · at its level · noisy
Chess Puzzles (Epoch)	24.0% ✎	█▁▁▁▁▂▁▄▂▂▁▁ beats 92/135	very hard · avg 5.9%	▇ 3.7%	sharp · noisy
The Token Games Elo (10-model puzzle-duel tournament)	1057.0 ✎	█··▄█··▄·▄·▄ beats 3/7	avg 815	▇ 3.4%	typical
WildClawBench	19.3% ✎	▃·▅▅█·▅█▅▃▃▃ beats 0/17	very hard · avg 11%	▇ 3.3%	at its level
SimpleQA Verified	30.2% ✎	▁▁▃▂▃▃▆▆█▃▂▅ beats 19/78	very hard · avg 8.0%	▇ 2.2%	at its level

$ where to run it

Through an API

api · xAI · $1.56/M · $1.25 in / $2.5 out per 1M tokens · 1M context · docs.x.ai ↗ · price sourceStandard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ.

$ how the score was worked out · Epoch AI — internal evaluatio… 57%, LMArena leaderboard dataset 22%, ARC Prize Foundation model_ba… 10% and 3 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

source	results	share of the score	licence
Epoch AI — internal evaluations	6	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%	CC BY 4.0
LMArena leaderboard dataset	4	▇▇▇▇▇▇▇ 22%	CC BY 4.0
ARC Prize Foundation model_baseline outputs on ARC-AGI-1/2 public evaluation sets	2	▇▇▇ 10%	MIT
τ²/τ³-bench leaderboard: Sierra's own evaluation runs	1	▇ 4%	MIT
The Token Games: Evaluating Language Model Reasoning with Puzzle Duels — authors' own tournament	1	▇ 3%	CC BY-SA 4.0
WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation (arXiv:2605.10912v1)	1	▇ 3%	CC BY 4.0
prior · pull toward the middle		▇ 0%
weighted average		= 71.6