xAI · Frontier
Grok 4.6
Grok 4.6 is SpaceXAI's frontier reasoning model for coding, knowledge work, and STEM. It supports text, image, and file inputs with tool calling and structured outputs. Reasoning is always active, and requests above 200k input tokens use higher long-context rates. Content policy rejections can still be charged: SpaceXAI may pass through a $0.05 moderation-failure fee, or a $0.055 usage-guidelines violation fee, depending on which rejection upstream returns.
- Reasoning
- Tools
- Vision
- Structured output
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- low · medium · high · xhigh
- Discovery tier
- Frontier
Pricing
USD per 1M tokens- Input
- $2.00
- Output
- $6.00
- Cache read
- $0.50
Reference prices as published by the model provider. API example
Specifications
- Context window
- 500K
- Max output
- —
- Average output (7 days)
- 1.8K tokens
- Longer than 89% of 473 models
Side by side
Grok 4.6 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Grok 4.6xAIThis model | Kimi K3 TEEMoonshotRemove | |
|---|---|---|
| Provider | xAI | Moonshot |
| Discovery tier | Frontier | Open weights |
| Added | Aug 12, 2026 | Jul 31, 2026 |
| Limits | ||
| Context window | 500K | 1.05MBest |
| Max output | — | 65.54K |
| Average output | 1.8K tokens | 1.1K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.00Best | $3.00 |
| Output | $6.00Best | $15.00 |
| Cache read | $0.50Best | $1.50 |
| Performance | ||
| Throughput | 155.2 tok/sBest | 87.4 tok/s |
| Time to first token | 3.0 sBest | 3.2 s |
| Request success (24h) | 100.0%Best | 98.0% |
| Benchmark indexes | ||
| Intelligence Index | 44.3Best | 43.6 |
| Coding Index | 76.8Best | 76.2 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | low, medium, high, xhigh | low, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Grok 4.6
- Kimi K3 TEE
Intelligence Index
- Grok 4.644.3
- Kimi K3 TEE43.6
Grok 4.6 tested at high reasoning effort
Grok 4.6: Better than 90% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Grok 4.6: 76.8Top
- Kimi K3 TEE: 76.2
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Grok 4.6: 66.7%Top
- Kimi K3 TEE: 58.3%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Grok 4.6: 32.7%Top
- Kimi K3 TEE: 24.8%
Harvey LAB-AAv1.1
Legal tasks passed in full, with no material hallucination
- Grok 4.6: —
- Kimi K3 TEE: 5.3%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Grok 4.6: 1,531 EloTop
- Kimi K3 TEE: 1,499 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Grok 4.6: 1,618 EloTop
- Kimi K3 TEE: 1,534 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Grok 4.6: 17.0%
- Kimi K3 TEE: 22.0%Top
AA-LCRv1.1
Reasoning across long documents
- Grok 4.6: 80.3%
- Kimi K3 TEE: 88.7%Top
MLCR-AA
Long-context reasoning over medical records
- Grok 4.6: 15.6%
- Kimi K3 TEE: 21.7%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Grok 4.6: 42.9%
- Kimi K3 TEE: 46.9%Top
GPQA Diamond
Graduate-level science questions
- Grok 4.6: 94.9%Top
- Kimi K3 TEE: 93.5%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Grok 4.6: 21.2%Top
- Kimi K3 TEE: 12.6%
SciCode
Scientific computing in Python
- Grok 4.6: 56.5%
- Kimi K3 TEE: 59.5%Top
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Grok 4.6: 80.3%
- Kimi K3 TEE: 88.7%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Grok 4.6 was tested at high reasoning effort; compare scores measured at the same effort.Artificial Analysis
- Grok 4.6
- Kimi K3 TEE
| Leaderboard | Grok 4.6 | Kimi K3 TEE |
|---|---|---|
| Coding | #14 · 17.4% | Not in top 20 |
Ranks among Noumen catalog models on Use cases leaderboards. Only top-20 placements are listed.
- Grok 4.6
- Kimi K3 TEE
| Leaderboard | Grok 4.6 | Kimi K3 TEE |
|---|---|---|
| Vision Style Control · Humor | #12 · 1270.6 | Not in top 20 |
| WebDev · Image To Webdev | #11 · 1603.5 | Not in top 20 |
| Document Overall | #19 · 1452.3 | Not in top 20 |
| Document Style Control Overall | #18 · 1461.1 | Not in top 20 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
- Grok 4.6
- Kimi K3 TEE
| Leaderboard | Grok 4.6 | Kimi K3 TEE |
|---|---|---|
| FrontierCode Main | #9 · 48.0% | Not in top 20 |
| FrontierCode Extended | #7 · 61.3% | Not in top 20 |
Ranks among Noumen catalog models on FrontierCode leaderboards. Only top-20 placements are listed.FrontierCode
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 155.2 tok/s
- Faster than 76% of 256 models
- Time to first token
- 3.0 s
- Quicker than 51% of 256 models
- Prompt cache hits
- 85.2%
- About $0.72 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "x-ai/grok-4.6",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="x-ai/grok-4.6",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)xAI
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