API Pricing Comparison
Per-million-token costs for China's leading open-source coding models — where LongCat-2.0's pricing is one of its strongest differentiators today.
Standard API Pricing (USD per 1M tokens)
| Model | Input ($/M) | Output ($/M) | Cached Context | Notes |
|---|---|---|---|---|
| LongCat-2.0 (standard) | 0.75 | 2.95 | Free on cache hit | 1M context; agentic coding focus |
| LongCat-2.0 (promo) | 0.30 | 1.20 | Free on cache hit | Launch promotion pricing |
| GLM-5.1 | ~1.40+ | Varies by tier | Check provider | Frontend / web-agent scenarios from ~$1.40/M input |
| Kimi K2.6 | ~0.95+ | Varies by tier | Check provider | 256K context; long-session agent workloads |
| DeepSeek V4-Pro | Check provider | Check provider | Check provider | Pricing varies by API gateway; compare at OpenRouter |
| Qwen3.6 Plus | Check provider | Check provider | Check provider | Often positioned for cost-efficient terminal tasks |
Prices reflect publicly reported standard and promotional rates. Always verify current pricing on longcat.ai, OpenRouter, or your API provider before budgeting.
Example Workload Costs
Estimated cost for a typical agentic coding session: 100K input + 20K output tokens (e.g., multi-file refactor with tool calls).
| Model | Standard Rate | Promo Rate (LongCat only) |
|---|---|---|
| LongCat-2.0 | $0.075 + $0.059 = ~$0.13 | $0.030 + $0.024 = ~$0.05 |
| GLM-5.1 (input ~$1.40/M) | ~$0.14+ input alone | — |
| Kimi K2.6 (input ~$0.95/M) | ~$0.095+ input alone | — |
For high-volume agent pipelines with repeated context (system prompts, codebase snapshots), LongCat's free cached-context reads can reduce effective input cost significantly — a factor flat per-token tables often miss.
When Price Matters Most
- Agent loops: Multi-turn tool calling inflates token counts — output pricing dominates
- Long-context RAG: 1M context models send large prompts; cache hits save real money
- Production scale: At millions of tokens/day, $0.30 vs $0.95/M input is a 3× difference
- Evaluation & fine-tuning: Cheap inference enables more iteration on prompts and agents
Price is not everything — benchmark fit matters too. See our positioning guide and head-to-head pages for performance trade-offs.