Kimi K2 (Groq) vs Sonar Deep Research: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for Kimi K2 (Groq) (Groq) and Sonar Deep Research (Perplexity). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 10, 2026
Interactive Cost Calculator
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Showing costs for 2 models. Cheapest: Kimi K2 (Groq) at $25.00/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestKimi K2 (Groq) | Groq | $25.00 | $0.002500 | $10.00 | $15.00 | 58.3% | Alerts coming soon |
Sonar Deep Research | Perplexity | $60.00 | $0.006000 | $20.00 | $40.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | Kimi K2 (Groq) | Sonar Deep Research | Cheaper |
|---|---|---|---|
| Input (standard) | $1.00/M | $2.00/M | Kimi K2 (Groq) |
| Output | $3.00/M | $8.00/M | Kimi K2 (Groq) |
Prices last verified: 2026-03-06 – 2026-03-10
Cost Breakdown by Usage Volume
Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.
| Volume | Kimi K2 (Groq)Monthly | Sonar Deep ResearchMonthly | Kimi K2 (Groq)Per request | Sonar Deep ResearchPer request |
|---|---|---|---|---|
| 1K requests/mo | $2.50 | $6.00 | $0.002500 | $0.006000 |
| 10K requests/mo | $25.00 | $60.00 | $0.002500 | $0.006000 |
| 100K requests/mo | $250.00 | $600.00 | $0.002500 | $0.006000 |
| 1M requests/mo | $2,500.00 | $6,000.00 | $0.002500 | $0.006000 |
Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.
Price History
Input price per 1M tokensWhen to Choose Kimi K2 (Groq)
by Groq
- Code generation
- RAG / Semantic search
Input / 1M tokens
$1.00/M
Output / 1M tokens
$3.00/M
Context window
256,000
Tier
premium
When to Choose Sonar Deep Research
by Perplexity
- RAG / Semantic search
- Document summarization
Input / 1M tokens
$2.00/M
Output / 1M tokens
$8.00/M
Context window
127,072
Tier
reasoning
Key Differences Beyond Price
Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.
| Capability | Kimi K2 (Groq) | Sonar Deep Research |
|---|---|---|
| Context window | 256,000 tokens | 127,072 tokens |
| Max output tokens | 32,768 tokens | 8,192 tokens |
| Performance tier | Premium | Reasoning |
| Vision / image input | No | No |
| Function calling | Yes | No |
| JSON mode | Yes | No |
| Prompt caching | No | No |
| Batch API (50% discount) | No | No |
| Extended reasoning | No | Yes |
| Fine-tuning | No | No |
| Rate limit (req/min) | 30 | Not published |
Kimi K2 (Groq) notes
Moonshot AI's flagship model on Groq hardware.
Sonar Deep Research notes
Additional fees: $2/1M citation tokens, $3/1M reasoning tokens, $5 per 1,000 search queries. Designed for exhaustive research tasks.
Frequently Asked Questions
Is Kimi K2 (Groq) cheaper than Sonar Deep Research?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Kimi K2 (Groq) costs $250.00/month versus $600.00/month for Sonar Deep Research — a 58% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.
Which model has a larger context window, Kimi K2 (Groq) or Sonar Deep Research?
Kimi K2 (Groq) has a larger context window at 256,000 tokens, compared to 127,072 tokens for Sonar Deep Research. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do Kimi K2 (Groq) and Sonar Deep Research support the Batch API?
Neither Kimi K2 (Groq) nor Sonar Deep Research currently supports a batch API with discounted pricing. For batch-eligible alternatives, consider models from OpenAI, Anthropic, or Google that include batch API support.
Which model offers better prompt caching?
Neither Kimi K2 (Groq) nor Sonar Deep Research currently supports prompt caching. For prompt-caching capable alternatives, consider Claude models from Anthropic or GPT-4o from OpenAI.
What are the best use cases for Kimi K2 (Groq) vs Sonar Deep Research?
Both models are well-suited for RAG / Semantic search. Kimi K2 (Groq) is particularly strong for Code generation. Sonar Deep Research is favored for Document summarization. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.
What is the cost per request for Kimi K2 (Groq) vs Sonar Deep Research?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — Kimi K2 (Groq) costs $0.002500 per request and Sonar Deep Research costs $0.006000 per request. At 100,000 requests/month, that translates to $250.00 and $600.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
Kimi K2 (Groq) vs Sonar Deep Research: Summary
When comparing Kimi K2 (Groq) and Sonar Deep Research for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Kimi K2 (Groq) offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $250.00/month, compared to $600.00/month for Sonar Deep Research.
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. Kimi K2 (Groq) charges $1.00/M for input tokens and $3.00/M for output tokens. Sonar Deep Research charges $2.00/M input and $8.00/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.
Context window capacity differs between the two: Kimi K2 (Groq) supports up to 256,000 tokens in a single request, versus 127,072 tokens for Sonar Deep Research. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.
Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.
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