Parasail
Serverless inference — Kimi K3, GLM-5.3, MiniMax M3, DeepSeek V4, Qwen3.5, Llama 4
Parasail is a serverless LLM inference platform offering a broad catalog of open-weight models including Kimi K3, Kimi K2.6, GLM-5.3, GLM-5.3 Flash, MiniMax M3, DeepSeek V4 Pro/Flash, Qwen3.5 397B-A17B, Qwen3 235B-A22B, Llama 4 Maverick, Llama 3.3 70B, Mistral Small 3.2 24B, MiMo v2.5, Gemma 4 31B, gpt-oss-120b, and gpt-oss-20b. Pricing is published at parasail.io/pricing.
One of the broadest serverless open-weight catalogs available — Kimi K3, GLM-5.3, MiniMax M3, DeepSeek V4, Qwen3.5, and Llama 4 Maverick at competitive per-token rates.
- Broad open-weight model catalog: Kimi K3, GLM-5.3, MiniMax M3, DeepSeek V4, Qwen3.5, Llama 4
- Competitive serverless pricing across all tiers
- Kimi K3 and Kimi K2.6 availability — rare outside Chinese providers
- gpt-oss-120b and gpt-oss-20b at competitive rates
- OpenAI-compatible API
- Newer platform — less battle-tested than Together AI or Fireworks
- No proprietary frontier models
- Smaller community and ecosystem
Parasail Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
gpt-oss-20b gpt-oss | Chat | budget | $0.04/ $0.20 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
mistral-small-3-2-24b Mistral Small | Chat | budget | $0.09/ $0.30 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
gpt-oss-120b gpt-oss | Chat | budget | $0.10/ $0.75 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
deepseek-v4-flash DeepSeek V4 | Chat | efficient | $0.14/ $0.28 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
qwen3-235b-a22b-2507 Qwen3 | Chat | advanced | $0.14/ $0.80 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
mimo-v2-5 MiMo-V2.5 | Chat | efficient | $0.14/ $0.28 in / out per 1M EST Oct 2026 | — | — | — | 33K | — | ||
glm-5-3-flash GLM 5 | Chat | efficient | $0.15/ $0.50 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
gemma-4-31b Gemma 4 | Chat | efficient | $0.15/ $0.40 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
llama-3-3-70b Llama 3.3 | Chat | efficient | $0.22/ $0.50 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
minimax-m3 MiniMax M3 | Chat | advanced | $0.30/ $1.20 in / out per 1M EST Oct 2026 | — | — | — | 1.0M | — | ||
llama-4-maverick Llama 4 | Chat | advanced | $0.35/ $1.00 in / out per 1M EST Oct 2026 | — | — | — | 1.0M | — | ||
qwen3-5-397b-a17b Qwen3.5 | Chat | advanced | $0.50/ $3.60 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
kimi-k2-6 Kimi K2 | Chat | advanced | $0.75/ $3.50 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
glm-5-3 GLM 5 | Chat | advanced | $1.40/ $4.40 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
deepseek-v4-pro DeepSeek V4 | Chat | frontier | $1.74/ $3.48 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
kimi-k3 Kimi K3 | Chat | frontier | $3.00/ $15.00 in / out per 1M EST Oct 2026 | — | — | — | 128K | — |
Parasail — Frequently Asked Questions (FAQs)
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Full comparison tableParasail LLM pricing overview
Parasail is an inference API provider that hosts open-weight and third-party large language models. Parasail is a serverless LLM inference platform offering a broad catalog of open-weight models including Kimi K3, Kimi K2.6, GLM-5.3, GLM-5.3 Flash, MiniMax M3, DeepSeek V4 Pro/Flash, Qwen3.5 397B-A17B, Qwen3 235B-A22B, Llama 4 Maverick, Llama 3.3 70B, Mistral Small 3.2 24B, MiMo v2.5, Gemma 4 31B, gpt-oss-120b, and gpt-oss-20b. Pricing is published at parasail.io/pricing. Common use cases include Open-source, Cost-efficiency, Reasoning, Chat. Headquartered in United States, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Parasail vs other LLM providers
Parasail competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. One of the broadest serverless open-weight catalogs available — Kimi K3, GLM-5.3, MiniMax M3, DeepSeek V4, Qwen3.5, and Llama 4 Maverick at competitive per-token rates. Use the LLM provider comparison tool to see Parasail token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Parasail?
Parasail stands out because: One of the broadest serverless open-weight catalogs available — Kimi K3, GLM-5.3, MiniMax M3, DeepSeek V4, Qwen3.5, and Llama 4 Maverick at competitive per-token rates.. The API currently offers 16 models, spanning 2 frontier, 6 advanced, 5 efficient, 3 budget tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Parasail token pricing
Parasail charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.04/1M tokens. Prices shown are sourced from Parasail's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Parasail context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Parasail's largest context window is 1.0M tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Parasail makes sense
Managed inference APIs like Parasail eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Parasail API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Parasail token price data is available in the LLM price history charts.
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