Model profile
SpaceXAI
New in 2026

Grok 4.20 0309 (Reasoning)

Grok 4.20 0309 (Reasoning) is a mid-range-priced text-first model from SpaceXAI with partial runtime data, partial context coverage, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningN/A latencyN/A contextMid-range pricing
Intelligence
37.4

Benchmark blend

Coding
N/A

Dev workflow signal

Context
N/A

N/A

Input Price
$2.00

Mid-range tier

Decision snapshot
45

Grok 4.20 0309 (Reasoning) currently reads as a mid-range text-first option with partially published context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
N/A
Price tier
Mid-range
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
37.4
37

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$2.00
62

$6.00 output / 1M

Competitive

Editorial Profile

Grok 4.20 0309 (Reasoning) in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score N/AMath score N/A

The Grok 4.20 0309 (Reasoning) AI model by SpaceXAI.

Identity

SpaceXAI text-first profile

Positioning

Agent workflows / Reasoning with partially published context and partially published runtime.

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • Costs look manageable, but still deserve attention in always-on agents or batch jobs.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context limits are only partially published, so long-session planning needs extra validation.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
37.4
GPQA
88.5%
HLE
32.4%
Coding

Software implementation, debugging quality, and coding benchmark signal.

SciCode
44.7%

Extra benchmark cuts are not available for this category yet.

Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
82.9%
TAU2
96.5%
TerminalBench Hard
40.9%
LCR
60.7%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$2.00
Output
per 1M output tokens
$6.00
Blended
AA 3:1 mix
$3.00

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.