Model profile
SpaceXAI

Grok 3

Grok 3 is a mid-range-priced text-first model from SpaceXAI with partial runtime data, partial context coverage, and the clearest fit around coding / agent workflows.

Best for: Coding / Agent workflowsN/A latencyN/A contextMid-range pricing
Intelligence
18.6

Benchmark blend

Coding
0.425

Dev workflow signal

Context
N/A

N/A

Input Price
$4.00

Mid-range tier

Decision snapshot
35

Grok 3 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
Coding / Agent workflows
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
18.6
19

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
$4.00
62

$20.00 output / 1M

Competitive

Editorial Profile

Grok 3 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 43Math score 58

The Grok 3 AI model by SpaceXAI.

Identity

SpaceXAI text-first profile

Positioning

Coding / Agent workflows 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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Intelligence
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Context
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Input Price
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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
18.6
MMLU Pro
79.9%
GPQA
69.3%
HLE
4.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.425
SciCode
36.8%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
58.0
AIME
33.0%
AIME 2025
58.0%
Math 500
87.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
46.9%
TAU2
48.8%
TerminalBench Hard
11.4%
LCR
56.7%

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
$4.00
Output
per 1M output tokens
$20.00
Blended
AA 3:1 mix
$8.00

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.