Which is cheaper, DeepSeek-V3 0324 or Inkling-Small?
As of August 20, 2026, DeepSeek-V3 0324 is 5% cheaper on blended LLM API pricing. DeepSeek-V3 0324 costs $0.28 per million input tokens and $1.14 per million output tokens ($1.42 blended 1M-in + 1M-out). Inkling-Small costs $0.30 / $1.20 per million tokens ($1.50 blended). Sources: https://api-docs.deepseek.com/quick_start/pricing and https://thinkingmachines.ai. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does DeepSeek-V3 0324 vs Inkling-Small cost per million tokens?
DeepSeek-V3 0324 DeepSeek API pricing is $0.28 input and $1.14 output per million tokens via DeepSeek. Inkling-Small Thinking Machines Lab API pricing is $0.30 input and $1.20 output per million tokens via Thinking Machines Lab. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, DeepSeek-V3 0324 or Inkling-Small?
Inkling-Small has a published SWE-bench Verified score of 80.2%. DeepSeek-V3 0324 does not have that eval on this page yet.
What is the context window for DeepSeek-V3 0324 vs Inkling-Small?
DeepSeek-V3 0324 supports a 164K token context window, while Inkling-Small supports 256K tokens. Inkling-Small offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, DeepSeek-V3 0324 or Inkling-Small?
Inkling-Small leads on 9 of 9 shared benchmarks versus DeepSeek-V3 0324's 0 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is DeepSeek-V3 0324 or Inkling-Small better for production use?
Both DeepSeek-V3 0324 and Inkling-Small are production API models. For cost-sensitive production traffic, DeepSeek-V3 0324 has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between DeepSeek-V3 0324 and Inkling-Small in my app?
Yes. Call DeepSeek-V3 0324 through DeepSeek and Inkling-Small through Thinking Machines Lab with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this DeepSeek-V3 0324 vs Inkling-Small pricing data?
List rates are the latest published DeepSeek API pricing and Thinking Machines Lab API pricing, quoted per million tokens. Official rate sheets: https://api-docs.deepseek.com/quick_start/pricing and https://thinkingmachines.ai. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for DeepSeek-V3 0324 and Inkling-Small?
DeepSeek-V3 0324 supports up to 164K output tokens per request, while Inkling-Small supports up to 256K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, DeepSeek-V3 0324 or Inkling-Small?
Neither DeepSeek-V3 0324 nor Inkling-Small has published throughput data on this page yet. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this DeepSeek-V3 0324 vs Inkling-Small comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open DeepSeek-V3 0324 or Inkling-Small from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
