Last updated 2026-05-13. Pricing, model names, and provider policies change frequently.
Quick answer
Reduce LLM API costs by shrinking prompts, limiting output length, caching repeated context, routing simple tasks to cheaper models, batching where possible, and using evals to confirm quality does not regress. Cost optimization should be measured, not guessed.
Cut tokens before switching providers
Shorter prompts, smaller retrieved chunks, concise system instructions, and output limits often reduce spend without changing providers. This is usually the lowest-risk first step.
Route by task difficulty
Not every request needs the strongest model. Classification, extraction, formatting, and simple summarization may work well on cheaper or faster models if your evals confirm quality. For some teams, DeepSeek-V4-Flash becomes part of this routing layer because it can reduce spend without forcing a completely different provider shape.
Use caching and batching carefully
Prompt caching can help when the same context repeats. Batch APIs can reduce cost for offline jobs. Both require workflow fit; they are not automatic wins for every product.
Provider examples to compare
| Provider | Category | Supported models | OpenAI-compatible | Starting price | Context | Tool calling | Vision | Streaming | Status | Trust | Links |
|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenAI | Official APIs | GPT, reasoning models, embeddings, image | Yes | Budget to premium GPT tiers | Short to very long, model based | Yes | Yes | Yes | Available | 12/15 | |
| DeepSeek | Official APIs | DeepSeek-V4-Flash, DeepSeek-V4-Pro | Yes | Low-cost flash to discounted pro tiers | 1M context, up to 384K output | Yes | No | Yes | Available | 11/15 | |
| Google Gemini | Official APIs | Gemini, embedding models, multimodal models | Yes | Low-cost flash to premium tiers | Short to million-token-class options | Yes | Yes | Yes | Available | 11/15 | |
| DeepInfra | Inference Providers | Llama, Qwen, DeepSeek-V4, Mistral | Yes | Often low for open models | Broad open-model range, model specific | No | Yes | Yes | Available | 10/15 | |
| Groq | Inference Providers | Llama, Mixtral, Gemma, Whisper-like speech models | Yes | Speed-oriented model tiers | Selected fast-serving model range, model specific | Yes | No | Yes | Available | 11/15 | |
| Portkey | LLM API Aggregators | GPT, Claude, Gemini, DeepSeek-V4 | Yes | Plan dependent plus provider spend | Provider dependent | No | Yes | Yes | Available | 11/15 |
Checklist
- Set max output tokens and stop conditions.
- Remove unnecessary history, retrieved context, and verbose instructions.
- Use cheaper models for low-risk subtasks after evals pass.
- Track cost per successful task, not just cost per request.
Recommended next step
Estimate current spend, then test one optimization at a time against a quality eval set.
FAQ
What is the fastest way to lower cost?
Limit output length and remove unnecessary prompt/context tokens. These changes are simple and often effective.
Should I switch to a cheaper provider first?
Usually not first. Optimize prompt and routing, then compare providers with the same eval set.
Can caching hurt quality?
Caching repeated context usually should not, but stale context or incorrectly reused prompts can create product errors.