Grok Code Fast 1 xAI
💰 Total Cost Calculation (from Plugin)
Output: $0.000750
Output: $0.000750
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 10,000 input tokens and 500 output tokens:
- Input Cost: $0.002000
- Output Cost: $0.000750
- Total Cost: $0.001400
- Cost per 1K tokens: $0.000133
- Tokens per dollar: 7,500,000 tokens
- Context Window: 256000 tokens
Speed & Performance Analysis
With a processing speed of 700 tokens per second and 110ms time to first token:
- Processing Time: 16.23 seconds
- Latency: 110 milliseconds to first token
- Base Throughput: 700 tokens/second
- Effective Throughput: 654 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Grok Code Fast 1| Rank | AI Model & Provider | Total Cost | vs Grok Code Fast 1 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.000475 Best Value | ↓ 66.1% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.001563 | ↑ 11.6% more |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.002225 | ↑ 58.9% more |
| #4 |
Gemini 2.5 Flash
Google
|
$0.002225 | ↑ 58.9% more |
| #5 |
Mistral Large 3
Mistral AI
|
$0.002375 | ↑ 69.6% more |
| #6 |
Gemini 3.1 Flash
Google
|
$0.003125 | ↑ 123.2% more |
| #7 |
Kimi K2.5
Moonshot AI
|
$0.003765 | ↑ 168.9% more |
| #8 |
Grok Build 0.1
xAI
|
$0.004250 | ↑ 203.6% more |
| #9 |
Gemini 3.8 Flash
Google
|
$0.004313 | ↑ 208% more |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.004688 | ↑ 234.8% more |
| #11 |
o4-mini Deep Research
OpenAI
|
$0.005250 (rounded ~ $0.01) | ↑ 275% more |
| #12 |
Grok 4.3
xAI
|
$0.005313 (rounded ~ $0.01) | ↑ 279.5% more |
| #13 |
Grok 4.20 Beta
xAI
|
$0.005313 (rounded ~ $0.01) | ↑ 279.5% more |
| #14 |
Kimi K2.6
Moonshot AI
|
$0.005586 (rounded ~ $0.01) | ↑ 299% more |
| #15 |
Kimi K2.7 Code
Moonshot AI
|
$0.005586 (rounded ~ $0.01) | ↑ 299% more |
| #16 |
Claude Haiku 4.5
Anthropic
|
$0.005750 (rounded ~ $0.01) | ↑ 310.7% more |
| #17 |
o4-mini
OpenAI
|
$0.005775 (rounded ~ $0.01) | ↑ 312.5% more |
| #18 |
GPT-5.6 Luna
OpenAI
|
$0.006250 (rounded ~ $0.01) | ↑ 346.4% more |
| #19 |
Gemini 3.6 Flash
Google
|
$0.008625 (rounded ~ $0.01) | ↑ 516.1% more |
| #20 |
Gemini 2.5 Pro
Google
|
$0.009063 | ↑ 547.3% more |
| #21 |
Gemini 3.5 Flash
Google
|
$0.009375 | ↑ 569.6% more |
| #22 |
Grok 4.6
xAI
|
$0.009500 | ↑ 578.6% more |
| #23 |
Grok 4.5
xAI
|
$0.009500 | ↑ 578.6% more |
| #24 |
Claude Sonnet 5
Anthropic
|
$0.011500 (rounded ~ $0.01) | ↑ 721.4% more |
| #25 |
Gemini 3.1 Pro
Google
|
$0.012500 (rounded ~ $0.01) | ↑ 792.9% more |
| #26 |
GPT-5.3 Codex Spark
OpenAI
|
$0.012688 (rounded ~ $0.01) | ↑ 806.3% more |
| #27 |
GPT-5.3 Instant
OpenAI
|
$0.012688 (rounded ~ $0.01) | ↑ 806.3% more |
| #28 |
GPT-5.4
OpenAI
|
$0.015625 (rounded ~ $0.02) | ↑ 1016.1% more |
| #29 |
GPT-5.4 Thinking
OpenAI
|
$0.015625 (rounded ~ $0.02) | ↑ 1016.1% more |
| #30 |
GPT-5.6 Terra
OpenAI
|
$0.015625 (rounded ~ $0.02) | ↑ 1016.1% more |
| #31 |
Claude Sonnet 4.6
Anthropic
|
$0.017250 (rounded ~ $0.02) | ↑ 1132.1% more |
| #32 |
Claude Opus 4.7
Anthropic
|
$0.028750 (rounded ~ $0.03) | ↑ 1953.6% more |
| #33 |
Claude Opus 5
Anthropic
|
$0.028750 (rounded ~ $0.03) | ↑ 1953.6% more |
| #34 |
Claude Opus 4.8
Anthropic
|
$0.028750 (rounded ~ $0.03) | ↑ 1953.6% more |
| #35 |
Claude Opus 4.6
Anthropic
|
$0.028750 (rounded ~ $0.03) | ↑ 1953.6% more |
| #36 |
GPT-5.5
OpenAI
|
$0.031250 (rounded ~ $0.03) | ↑ 2132.1% more |
| #37 |
GPT-5.5 Instant
OpenAI
|
$0.031250 (rounded ~ $0.03) | ↑ 2132.1% more |
| #38 |
GPT-5.6 Sol
OpenAI
|
$0.031250 (rounded ~ $0.03) | ↑ 2132.1% more |
| #39 |
Claude Fable 5.1
Anthropic
|
$0.051875 (rounded ~ $0.05) | ↑ 3605.4% more |
| #40 |
Claude Mythos 5.1
Anthropic
|
$0.051875 (rounded ~ $0.05) | ↑ 3605.4% more |
| #41 |
o3 Deep Research
OpenAI
|
$0.052500 (rounded ~ $0.05) | ↑ 3650% more |
| #42 |
Claude Fable 5
Anthropic
|
$0.057500 (rounded ~ $0.06) | ↑ 4007.1% more |
| #43 |
Claude Mythos 5
Anthropic
|
$0.057500 (rounded ~ $0.06) | ↑ 4007.1% more |
| #44 |
GPT-6 Astra
OpenAI
|
$0.057500 (rounded ~ $0.06) | ↑ 4007.1% more |
| #45 |
o3 Pro
OpenAI
|
$0.105000 (rounded ~ $0.11) | ↑ 7400% more |
| #46 |
GPT-5.2 Pro
OpenAI
|
$0.152250 (rounded ~ $0.15) | ↑ 10775% more |
| #47 |
GPT-5.2 Pro
OpenAI
|
$0.152250 (rounded ~ $0.15) | ↑ 10775% more |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.1 Flash Google
Kimi K2.5 Moonshot AI
Grok Build 0.1 xAI
Gemini 3.8 Flash Google
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Grok 4.3 xAI
Grok 4.20 Beta xAI
Kimi K2.6 Moonshot AI
Kimi K2.7 Code Moonshot AI
Claude Haiku 4.5 Anthropic
o4-mini OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
Gemini 3.5 Flash Google
Grok 4.6 xAI
Grok 4.5 xAI
Claude Sonnet 5 Anthropic
Gemini 3.1 Pro Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.5 OpenAI
GPT-5.5 Instant OpenAI
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
o3 Deep Research OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-6 Astra OpenAI
o3 Pro OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Optimizing Clinical Documentation Pipelines with Code Generation
For healthcare administrators managing clinical documentation systems, the choice of an AI coding assistant is about balancing speed, accuracy, and rigorous audit trails. Grok Code Fast 1 has emerged as a high-performance option for IDE integrations, particularly for teams requiring rapid, low-latency feedback during the development of medical records management software.
When working within a healthcare IT environment, the stability of the code generation process is paramount. Grok Code Fast 1 excels in this regard, offering a streamlined experience for inline code suggestions. Its architecture is specifically tuned for agentic workflows, meaning it handles complex, multi-file refactoring and bug fixing in legacy clinical databases with fewer interventions. This responsiveness is vital when maintaining HIPAA-compliant systems where every minute of downtime or developer friction impacts clinical workflows.
The model’s ability to handle 10K-token context windows effectively allows it to ingest large swathes of documentation logic and medical terminology mapping, ensuring suggestions are contextually aware of the specific healthcare standards being used. Administrators should consider this model when the priority is maintaining a high-velocity development cycle for internal tools. Unlike generalist models, this specialized approach reduces the likelihood of hallucinations in code, which is essential when building modules that interface with sensitive patient data or electronic health record (EHR) APIs. By integrating a model that understands the intricacies of clinical software development, teams can significantly improve their build-out efficiency while maintaining the necessary compliance oversight.