Use case

LLM API for Code Generation

Power code completion, generation, review, and refactoring features with the right model for each task, backed by failover and cost controls.

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You only pay credits per request. No monthly subscription. Paid credits never expire.

Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.

First success in 60 seconds
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Why teams start here first
No monthly subscription
Pay-as-you-go credits
Start with trial credits, then buy only what you consume.
Failover safety
Production-ready routing
Auto fallback across providers when latency, quality, or reliability changes.
Data control
Your policy, your choice
BYOK and zero-retention mode keep training and storage scope explicit.
Single API experience
One key, multi-provider access
Use Chat/Compare/Blend/Judge/Failover from one dashboard.
Common problem
Code generation demands high accuracy because even small errors produce broken builds or security vulnerabilities, but no single model is best at every programming language and task type.
Common problem
Developer tools require low latency and high availability because they sit in the critical path of the development workflow, and any interruption breaks the developer's flow state.
Common problem
Code generation features at scale generate significant token volume, and using a frontier model for every code completion request is prohibitively expensive when many completions are simple one-liners.

How LLMWise helps

✓Route each code task to the strongest model: GPT-5.2 for complex multi-file generation, Claude Sonnet 4.5 for nuanced refactoring and code review, and DeepSeek V3 for cost-efficient inline completions.
✓Mesh failover keeps code generation features responsive even during provider outages, which is critical for developer tools where a five-second stall feels like an eternity.
✓Compare mode lets you evaluate multiple models on the same coding prompt to find which produces the most correct, idiomatic code for your target language and framework.
✓BYOK support lets you use your own provider API keys for high-volume code completion endpoints, eliminating per-token markup while keeping LLMWise orchestration and failover.
Evidence snapshot

LLM API for Code Generation implementation evidence

Use-case readiness across problem fit, expected outcomes, and integration workload.

Problems mapped
3
pain points addressed
Benefits
4
outcome claims surfaced
Integration steps
4
path to first deployment
Decision FAQs
5
adoption blockers handled

Integration path

  1. Connect your developer tool's backend to the LLMWise API using the LLMWise SDK or REST calls. Use streaming for code completion features where perceived latency matters, and non-streaming for batch operations like code review.
  2. Configure model routing by task type: inline completions use a fast, cost-efficient model like DeepSeek V3 or Gemini 3 Flash, while multi-file generation and code review use GPT-5.2 or Claude Sonnet 4.5.
  3. Enable Mesh failover on all code generation endpoints. Set the fallback chain to cross providers, for example GPT-5.2 to Claude Sonnet 4.5 to Llama 4 Maverick, so no single provider outage disrupts your users.
  4. Use the Replay Lab to test new models against your historical code generation requests. Compare correctness and token efficiency before switching models in production to avoid regressions.
Example API call
POST /api/v1/chat
{
  "model": "auto",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "..."}
  ],
  "stream": true
}
Example workflow

A developer using your AI code assistant highlights a function and requests a refactor. Your tool's backend sends the code to LLMWise Chat mode with Claude Sonnet 4.5 specified for its strong refactoring capabilities. The model streams back the refactored code in under 400 milliseconds to first token. Meanwhile, a junior developer on the same team triggers an inline completion while typing a Python function. Auto mode detects this as a simple completion task and routes it to DeepSeek V3, which returns the suggestion in 150 milliseconds at a fraction of the cost. During a brief OpenAI outage, a user requests multi-file generation that would normally route to GPT-5.2. Mesh failover detects the failure instantly, switches to Claude Sonnet 4.5, and delivers the result with only a 200-millisecond delay — invisible to the developer.

Why LLMWise for this use case

Code generation tools face a unique combination of constraints: they need sub-second latency for inline completions, high accuracy for complex generation, and sustainable per-user economics at scale. LLMWise solves all three with intelligent routing that matches each code task to the right model — fast cheap models for completions, powerful models for generation and review — plus Mesh failover that keeps the developer experience seamless during provider outages. BYOK support makes high-volume deployment economically viable, and Compare mode gives your quality team a continuous benchmarking pipeline to validate model performance across languages and frameworks.

Common questions

Which LLM is best for code generation?
GPT-5.2 and Claude Sonnet 4.5 lead on complex multi-step code generation and refactoring. For high-volume inline completions where speed matters, DeepSeek V3 and Gemini 3 Flash deliver good results at much lower cost. LLMWise lets you use the right model for each task type.
Can LLMWise handle the latency requirements of code completion?
Yes. Streaming mode delivers the first token in under 300 milliseconds for most models. For code completion, pair a fast model like Gemini 3 Flash with Mesh failover so the user always gets a rapid response even if the primary model is slow.
Does LLMWise support function calling for code tools?
Not currently. Today, most teams implement tool workflows by prompting for structured JSON output and validating it in their app, then using Judge mode as a second-pass quality check. LLMWise focuses on multi-model orchestration, routing, and reliability rather than provider-specific tool-call schemas.
How do I build AI-powered code review into my developer tool?
Send the code diff or file contents to LLMWise Chat mode with a code review system prompt that specifies your coding standards, security rules, and style guidelines. Use Claude Sonnet 4.5 or GPT-5.2 for thorough review, and add Judge mode for a second-opinion check on critical repositories. For pull request review at scale, batch the requests and use the Usage API to track review cost per repository. Streaming mode lets you display review comments progressively as the model generates them.
How much does AI code generation cost per developer with LLMWise?
Cost varies by usage pattern. A typical developer generating 200 inline completions and 20 complex generation requests per day would use approximately 220 to 300 credits daily using Auto mode's intelligent routing. With BYOK mode, you pay only the underlying provider token costs with no LLMWise markup. Tiered routing — cheap models for completions, powerful models for generation — typically reduces per-developer cost by 40 to 60 percent compared to using a single frontier model for everything.

One wallet, enterprise AI controls built in

You only pay credits per request. No monthly subscription. Paid credits never expire.

Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.

Chat, Compare, Blend, Judge, MeshPolicy routing + replay labFailover without extra subscriptions
Start free with 40 creditsSee pricing examples
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