Grok 3 Reasoner

The grok/grok-3-reasoner represents a paradigm shift in artificial intelligence, moving beyond simple token prediction into deep, inference-time reasoning. By utilizing a chain-of-thought process, grok/grok-3-reasoner can self-correct, explore multiple logical paths, and verify its own conclusions before providing a final answer. On the GPT Proto platform, users gain immediate access to this sophisticated architecture, backed by low-latency infrastructure and professional-grade state management. Whether you are debugging kernel-level code or simulating complex economic theories, grok/grok-3-reasoner provides the cognitive heavy lifting required for mission-critical tasks.

Modalities

API Usage Examples

curl --request POST "https://gptproto.com/v1/chat/completions" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "grok-3-reasoner",
    "messages": [
      {
        "role": "user",
        "content": "Hello"
      }
    ]
  }'

Grok 3 Reasoner pricing

Start from the cost of a single sample and pick a testing budget. GPTProto rates are 40% below list price.

Your usage

Model

$0.0081 / image

Images per month

Scenario presets

Related Models

All Models

Grok 3 Reasoner

Current

byGrok$0.0082/M

DeepSeek Flash

byDeepSeek$0.3/M input$1.2/M output

Hy4 Preview

byHunyuan1.05M context$0.7923/M input$2.376/M output

GPT 6 Astra

byOpenAI1.05M context$8/M input$40/M output

Gemini 3.8 Flash

byGoogle1.05M context$0.9/M input$4.5/M output

Claude Fable 5.1

byClaude1M context$9/M input$45/M output

Qwen3.8 Max 0902

byQwen1M context$1.8/M input$5.4/M output

GLM 5.3 Flash

byZ-AI1.31M context$0.15/M input$0.5/M output

DeepSeek v4 Flash Vision Exp

byDeepSeek1.05M context$0.3/M input$1.2/M output

GLM 5.3

byZ-AI1.31M context$1.26/M input$3.96/M output

Gemini 3.7 Flash

byGoogle1.05M context$0.9/M input$4.5/M output

Grok 4.6

byGrok500K context$1.2/M input$3.6/M output

Qwen3.8 Max

byQwen1M context$1.8/M input$5.4/M output

Claude Opus 5

byClaude1M context$4.5/M input$22.5/M output

Gemini 3.6 Flash

byGoogle1.05M context$0.9/M input$4.5/M output

Kimi K3

byMoonshotAI1.05M context$2.7/M input$13.5/M output

GPT 5.6 Luna

byOpenAI1.05M context$0.16/M input$0.96/M output

GPT 5.6 Terra

byOpenAI1.05M context$1.6/M input$9.6/M output

Grok 4.5

byGrok500K context$1.2/M input$3.6/M output

Claude Sonnet 5

byClaude1M context$1.8/M input$9/M output

Minimax M3

byMiniMax1.05M context$0.48/M input$0.96/M output

GLM 5.2

byZ-AI1.05M context$1.26/M input$3.96/M output

Qwen3.7 Max

byQwen1M context$0.36/M input$1.44/M output

DeepSeek v4 Flash

byDeepSeek1.05M context$0.3/M input$1.2/M output

Grok 4.3

byGrok1M context$0.75/M input$1.5/M output

Kimi K2.6

byMoonshotAI262K context$0.855/M input$3.6/M output

Grok 4.20 Beta 0309 Reasoning

byGrok2M context$0.75/M input$1.5/M output

Grok 4.20 Beta 0309 Non Reasoning

byGrok2M context$0.75/M input$1.5/M output

Grok 4.20 Multi Agent Beta 0309

byGrok2M context$0.75/M input$1.5/M output

Minimax M2.5

byMiniMax205K context$0.24/M input$0.96/M output

Kimi K2.5

byMoonshotAI262K context$0.54/M input$2.7/M output

Doubao Seed 1.6 Thinking (Build 250715)

byBytedance262K context$0.0971/M input$0.9714/M output

Doubao Seed 1.6 Thinking (Build 250615)

byBytedance262K context$0.0971/M input$0.9714/M output

Doubao Seed 1.6 Flash (Build 250615)

byBytedance262K context$0.0182/M input$0.1821/M output

Model					Input → Output
Grok 3 ReasonerCurrent $		—	$0.0082 per 1M	—	Input: Text Output: Text
DeepSeek Flash $	—	—	$0.30 / $1.20 per 1M	— / $0.006 per 1M	Input: TextInput: Image Output: Text
Hy4 Preview $		1.05M	$0.79 / $2.38 per 1M	— / $0.04 per 1M	Input: Text Output: Text
GPT 6 Astra $		1.05M	$8.00 / $40.00 per 1M	$10.00 / $0.80 per 1M	Input: TextInput: ImageInput: Document Output: Text
Gemini 3.8 Flash $		1.05M	$0.90 / $4.50 per 1M	$0.60 / $0.09 per 1M	Input: TextInput: ImageInput: VideoInput: DocumentInput: Audio Output: Text
Claude Fable 5.1 $		1M	$9.00 / $45.00 per 1M	$11.25 / $0.23 per 1M	Input: TextInput: ImageInput: Document Output: Text
Qwen3.8 Max 0902 $		1M	$1.80 / $5.40 per 1M	$2.25 / $0.23 per 1M	Input: TextInput: ImageInput: VideoInput: DocumentInput: Audio Output: Text
GLM 5.3 Flash $	—	1.31M	$0.15 / $0.50 per 1M	— / $0.03 per 1M	Input: TextInput: ImageInput: VideoInput: Document Output: Text
DeepSeek v4 Flash Vision Exp $	—	1.05M	$0.30 / $1.20 per 1M	— / $0.006 per 1M	Input: TextInput: Image Output: Text
GLM 5.3 $		1.31M	$1.26 / $3.96 per 1M	— / $0.23 per 1M	Input: TextInput: ImageInput: Document Output: Text
Gemini 3.7 Flash $		1.05M	$0.90 / $4.50 per 1M	$0.60 / $0.09 per 1M	Input: TextInput: ImageInput: Document Output: Text
Grok 4.6 $		500K	$1.20 / $3.60 per 1M	— / $0.30 per 1M	Input: TextInput: Image Output: Text
Qwen3.8 Max $		1M	$1.80 / $5.40 per 1M	$2.25 / $0.23 per 1M	Input: TextInput: ImageInput: VideoInput: Document Output: Text
Claude Opus 5 $		1M	$4.50 / $22.50 per 1M	$5.63 / $0.45 per 1M	Input: TextInput: ImageInput: Document Output: Text
Gemini 3.6 Flash $		1.05M	$0.90 / $4.50 per 1M	$0.60 / $0.09 per 1M	Input: TextInput: ImageInput: Document Output: Text
Kimi K3 $		1.05M	$2.70 / $13.50 per 1M	$0.27 / $0.27 per 1M	Input: TextInput: ImageInput: Document Output: Text
GPT 5.6 Luna $		1.05M	$0.16 / $0.96 per 1M	$0.20 / $0.02 per 1M	Input: TextInput: ImageInput: Document Output: Text
GPT 5.6 Terra $		1.05M	$1.60 / $9.60 per 1M	$2.00 / $0.16 per 1M	Input: TextInput: ImageInput: Document Output: Text
Grok 4.5 $		500K	$1.20 / $3.60 per 1M	$0.30 / $0.30 per 1M	Input: TextInput: Image Output: Text
Claude Sonnet 5 $		1M	$1.80 / $9.00 per 1M	$2.25 / $0.18 per 1M	Input: TextInput: Document Output: Text
Minimax M3 $		1.05M	$0.48 / $0.96 per 1M	$0.10 / $0.10 per 1M	Input: TextInput: ImageInput: Document Output: Text
GLM 5.2 $		1.05M	$1.26 / $3.96 per 1M	$0.23 / $0.23 per 1M	Input: TextInput: ImageInput: Document Output: Text
Qwen3.7 Max $		1M	$0.36 / $1.44 per 1M	$0.07 / $0.07 per 1M	Input: TextInput: Document Output: Text
DeepSeek v4 Flash $	—	1.05M	$0.30 / $1.20 per 1M	— / $0.006 per 1M	Input: Text Output: Text
Grok 4.3 $		1M	$0.75 / $1.50 per 1M	$0.12 / $0.12 per 1M	Input: TextInput: Image Output: Text
Kimi K2.6 $		262K	$0.85 / $3.60 per 1M	$0.14 / $0.14 per 1M	Input: TextInput: Document Output: Text
Grok 4.20 Beta 0309 Reasoning $		2M	$0.75 / $1.50 per 1M	$0.12 / $0.12 per 1M	Input: TextInput: Image Output: Text
Grok 4.20 Beta 0309 Non Reasoning $		2M	$0.75 / $1.50 per 1M	$0.12 / $0.12 per 1M	Input: TextInput: Image Output: Text
Grok 4.20 Multi Agent Beta 0309 $		2M	$0.75 / $1.50 per 1M	$0.12 / $0.12 per 1M	Input: TextInput: Image Output: Text
Minimax M2.5 $		205K	$0.24 / $0.96 per 1M	$0.30 / $0.02 per 1M	Input: TextInput: Document Output: Text
Kimi K2.5 $		262K	$0.54 / $2.70 per 1M	$0.09 / $0.09 per 1M	Input: TextInput: Document Output: Text
Doubao Seed 1.6 Thinking (Build 250715) $		262K	$0.10 / $0.97 per 1M	—	Input: TextInput: Image Output: Text
Doubao Seed 1.6 Thinking (Build 250615) $		262K	$0.10 / $0.97 per 1M	—	Input: TextInput: Image Output: Text
Doubao Seed 1.6 Flash (Build 250615) $		262K	$0.02 / $0.18 per 1M	—	Input: TextInput: Image Output: Text

Strategic Intelligence Redefined: The grok/grok-3-reasoner on GPT Proto

Experience the pinnacle of inference-time scaling with grok/grok-3-reasoner, now fully integrated into the GPT Proto ecosystem. Explore the model library today to begin your journey with advanced logic.

Solving the 'Black Box' Logic Gap with grok/grok-3-reasoner

Traditional Large Language Models (LLMs) often struggle with multi-step reasoning, frequently falling into the trap of 'logical hallucinations' where the final output sounds confident but lacks foundational accuracy. The grok/grok-3-reasoner solves this by introducing a dedicated reasoning phase. Instead of rushing to generate the next word, grok/grok-3-reasoner creates an internal 'thinking trace' that allows it to simulate outcomes and backtrack when it encounters a logical dead end. On GPT Proto, we provide the raw power needed to sustain these long-inference cycles, ensuring that your most difficult queries are met with rigorous validation.

Technical users will appreciate the way grok/grok-3-reasoner handles stateful interactions. Unlike older APIs that require you to resend the entire conversation history—increasing latency and cost—the GPT Proto implementation of grok/grok-3-reasoner utilizes a sophisticated Responses API. This allows for conversation IDs to be stored on the server for up to 30 days, enabling you to append new prompts to an existing logical thread without the overhead of massive context re-submission.

Specific Use Case A: Deep Architectural Code Audits

Senior developers often find themselves reviewing thousands of lines of interconnected microservices. A standard model might miss a race condition three levels deep in the logic. By deploying grok/grok-3-reasoner, engineers can request a 'logical audit' of specific codebases. The model doesn't just look for syntax errors; it reasons through the execution flow. My experience shows that grok/grok-3-reasoner is particularly adept at identifying edge cases in concurrency and memory management that other models simply gloss over.

Specific Use Case B: Quantitative Risk Assessment

In the financial sector, variables are rarely static. When building risk models, analysts need a tool that can reason through secondary and tertiary effects of a market shift. Using grok/grok-3-reasoner allows analysts to input complex datasets and ask for a logical projection based on specific constraints. The grok/grok-3-reasoner excels here because it can cross-reference multiple mathematical theories within its thinking traces, ensuring that the final risk assessment is backed by a visible chain of logic.

"The move from fast-pattern matching to slow-inference reasoning is the single most important development in AI this year. grok/grok-3-reasoner is at the absolute forefront of this transition, providing the depth of thought previously reserved for human experts."

Optimization and Integration on GPT Proto

Integrating grok/grok-3-reasoner into your existing workflow is seamless on the GPT Proto platform. We provide comprehensive SDK support and a robust API that manages the complexities of stateful reasoning for you. Stability is our core promise; while others struggle with the high compute demands of reasoning models, GPT Proto ensures your grok/grok-3-reasoner instances remain responsive and consistent. For detailed implementation details, visit our technical documentation.

Feature	Standard LLM Models	grok/grok-3-reasoner on GPT Proto
Reasoning Method	Direct Token Prediction	Inference-Time Chain-of-Thought
State Management	Manual History Resending	Stateful Conversation IDs (30-day retention)
Context Window	Variable (often fragmented)	128k+ Optimized Tokens
Error Correction	Post-Generation Prompting	Real-time Self-Correction in Trace

Transparent Usage and Scalability

At GPT Proto, we believe in clarity. There are no hidden tiers or confusing credit systems when using grok/grok-3-reasoner. Our billing is built around a simple 'top-up' model. You can quickly Add Funds to your account to ensure uninterrupted access to grok/grok-3-reasoner. Monitor your real-time usage and manage your environment through your central User Dashboard. This transparency allows startups and enterprises alike to scale their reasoning capabilities without administrative friction.

As AI continues to evolve toward systemic logic, staying ahead of the curve is non-negotiable. By leveraging grok/grok-3-reasoner on GPT Proto, you are not just using a tool; you are adopting a logical framework that grows with your complexity. Read more about our latest updates on the GPT Proto Blog.

Deep Dive Inquiries: Navigating grok/grok-3-reasoner Implementation

Everything you need to know about deploying and optimizing the grok/grok-3-reasoner for your professional workflows.

What exactly makes grok/grok-3-reasoner different from a standard Grok 3 model?

The grok/grok-3-reasoner is specifically optimized for 'System 2' thinking, which involves a delibrate reasoning phase where the model explores different solutions before responding, unlike the standard Grok 3 which focuses on high-speed pattern matching.

How do I access my grok/grok-3-reasoner conversation history on GPT Proto?

GPT Proto stores your grok/grok-3-reasoner stateful interactions for 30 days via response IDs, allowing you to resume complex logical threads without resending the entire context.

Does grok/grok-3-reasoner support structured outputs like JSON?

Yes, grok/grok-3-reasoner is highly capable of generating valid structured outputs, using its reasoning phase to ensure the JSON schema is perfectly followed even in complex nested structures.

Can I see the internal 'thinking traces' of grok/grok-3-reasoner?

Yes, through the GPT Proto API, you can request encrypted thinking content from grok/grok-3-reasoner to analyze the logical path the model took during its inference phase.

How does grok/grok-3-reasoner handle mathematical proofs?

The grok/grok-3-reasoner uses its extended inference time to verify each step of a mathematical proof, significantly reducing the errors common in non-reasoning AI models.

Is there a limit to how many messages I can append to a grok/grok-3-reasoner thread?

While there is no strict message count limit, the grok/grok-3-reasoner is bound by its total token context window, though GPT Proto optimizes this via automatic caching.

Why should I use grok/grok-3-reasoner for coding tasks?

For coding, grok/grok-3-reasoner is superior because it simulates execution paths and can identify logical flaws in algorithms that standard models would overlook.

What is the billing model for grok/grok-3-reasoner on GPT Proto?

Usage of grok/grok-3-reasoner is billed based on tokens consumed. You can simply Top-up Balance in your billing center to keep your reasoning threads active.

Can I disable server-side storage for grok/grok-3-reasoner prompts?

Yes, if your security protocols require it, you can set 'store: false' when calling the grok/grok-3-reasoner API on GPT Proto to ensure no data is cached.

How does grok/grok-3-reasoner compare to other reasoning models?

grok/grok-3-reasoner stands out for its high integration with X (formerly Twitter) data and its unique xAI architecture which provides highly authentic and authoritative logical chains.

Can grok/grok-3-reasoner be used for real-time applications?

While grok/grok-3-reasoner takes longer to process due to its thinking phase, GPT Proto's optimized infrastructure ensures the lowest possible latency for a model of this depth.

What language support does grok/grok-3-reasoner offer?

grok/grok-3-reasoner supports all major global languages, though its advanced reasoning is currently most refined in English and common programming languages.

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Strategic Intelligence Redefined: The grok/grok-3-reasoner on GPT Proto

Solving the 'Black Box' Logic Gap with grok/grok-3-reasoner

Specific Use Case A: Deep Architectural Code Audits

Specific Use Case B: Quantitative Risk Assessment

Optimization and Integration on GPT Proto

Transparent Usage and Scalability

What exactly makes grok/grok-3-reasoner different from a standard Grok 3 model?

How do I access my grok/grok-3-reasoner conversation history on GPT Proto?

Does grok/grok-3-reasoner support structured outputs like JSON?

Can I see the internal 'thinking traces' of grok/grok-3-reasoner?

How does grok/grok-3-reasoner handle mathematical proofs?

Is there a limit to how many messages I can append to a grok/grok-3-reasoner thread?

Why should I use grok/grok-3-reasoner for coding tasks?

What is the billing model for grok/grok-3-reasoner on GPT Proto?

Can I disable server-side storage for grok/grok-3-reasoner prompts?

How does grok/grok-3-reasoner compare to other reasoning models?

Can grok/grok-3-reasoner be used for real-time applications?

What language support does grok/grok-3-reasoner offer?

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