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LLMS • CURATED • UPDATED JUL 9, 2026

Grok 4.5

xAI's flagship coding model, trained in partnership with Cursor

Grok 4.5 is xAI's flagship model, shipped July 8, 2026 with public rollout July 9. It has a 500K-token context window (with a high-context surcharge above 200K) and configurable reasoning effort (low/medium/high, defaulting to high). xAI trained it in partnership with Cursor specifically to handle long-running jobs across multiple repositories with minimal human intervention across hundreds of tool calls.

Pricing Paid
Platforms web, api
API No
Open Source No
Modalities LLMs, IDEs & Coding Tools
Best For Best for Long-Running Coding Agents
Date Added 2026-07-09

xAI positions it as its flagship 'for code and everything else,' and real benchmark data backs that up, 53.8% on the Artificial Analysis Intelligence Index (rank 7 overall). The Cursor training partnership is a distinctive angle: it's specifically tuned for long-horizon, multi-repository coding agent work, not just general chat.

Access Grok 4.5 via the xAI API or Grok apps. Pricing is $2/M input tokens, $6/M output tokens, with cached input at $0.50/M, a high-context surcharge applies above 200K tokens. Set the reasoning_effort parameter (low/medium/high) to balance cost against task difficulty; high is the default.

1 Use reasoning_effort=low for simple tasks to control cost
2 Take advantage of the Cursor-trained coding-agent behavior for multi-repo work
3 Watch for the high-context surcharge above 200K tokens
4 Use cached input pricing where your workflow repeats context
5 Design long-running agent tasks with clear checkpoints given its multi-tool-call design
Google Antigravity 2.0 Claude Fable 5 NotebookLM Claude Opus 5 GPT-5.6 Sol

Long-Running Multi-Repository Coding Agent

Run an autonomous coding agent across multiple repositories with minimal supervision.

STEPS:
  1. Connect Grok 4.5 to your repositories via API
  2. Define the overall task and constraints
  3. Set reasoning_effort based on task difficulty
  4. Let the agent execute across hundreds of tool calls
  5. Review checkpoints and final output
  6. Run tests before merging any changes

Cost-Tuned General Reasoning

Balance reasoning quality against cost using the configurable effort setting.

STEPS:
  1. Identify the task's actual difficulty
  2. Set reasoning_effort to match (low/medium/high)
  3. Run the task and review output quality
  4. Adjust effort level if quality doesn't meet expectations
  5. Track cost per task across effort levels
Paid

Requires a paid subscription.

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Compare Grok 4.5 with 5+ similar llms AI tools.

Q

Is Grok 4.5 free?

A

No, Grok 4.5 requires a paid subscription.

Q

Does Grok 4.5 have an API?

A

No, Grok 4.5 does not appear to offer a public API.

Q

What is Grok 4.5 best for?

A

Grok 4.5 is best for Best for Long-Running Coding Agents. Grok 4. xAI positions it as its flagship 'for code and everything else,' and real benchmark data backs that up, 53.8% on the Artificial Analysis Intelligence Index (rank 7 overall). The Cursor training partnership is a distinctive angle: it's specifically tuned for long-horizon, multi-repository coding agent work, not just general chat.

Q

What platforms does Grok 4.5 support?

A

Grok 4.5 supports web, api.

Q

Is Grok 4.5 open source?

A

No, Grok 4.5 is not open source.

Q

How do I get started with Grok 4.5?

A

Access Grok 4.5 via the xAI API or Grok apps. Pricing is $2/M input tokens, $6/M output tokens, with cached input at $0.50/M, a high-context surcharge applies above 200K tokens. Set the reasoning_effort parameter (low/medium/high) to balance cost against task difficulty; high is the default.

Q

How do I use Grok 4.5?

A

Grok 4.5 is a large language model for text generation, analysis, and conversation. Access through the web interface. Enter prompts or questions to get responses. It excels at 53.8% on the artificial analysis intelligence index (rank 7 overall).

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