MODEL COMPARISON

DeepSeek, Qwen, GLM, or Kimi: how to choose an API model

Use one repeatable evaluation workflow instead of selecting a model from vendor reputation alone.

Updated 2026-09-11

In brief

There is no single best model family for every application. TokenAAS lets teams compare configured DeepSeek, Qwen, GLM, and Kimi public model IDs behind the same authentication and usage layer, then select by task quality, first-token latency, total latency, price, and live capacity.

Start with the production requirement

Define the output contract before testing models. A customer-support assistant, extraction pipeline, coding workflow, and long-form content system may rank the same models differently.

  • Required language and output format
  • Acceptable first-token and total latency
  • Maximum cost per successful task
  • Tool use, streaming, and fallback requirements

Model families currently represented

TokenAAS catalog entries include configured or planned IDs across these families. Live status and API-key visibility remain authoritative because a listed model is not automatically routable.

  • DeepSeek: deepseek-v4-flash and deepseek-v4-pro
  • Qwen: qwen3.7-flash and other configured Qwen IDs
  • GLM: glm-5.2 and glm-5.3
  • Kimi: kimi-k2.6 and other configured Kimi IDs

A fair comparison method

Send the same representative prompts to every candidate and record results in the same time window. Test both normal and streaming responses when your application uses streaming.

  • Score task correctness with a fixed rubric
  • Measure first-token latency, total latency, and error rate
  • Record input and output tokens plus effective cost
  • Repeat across peak and off-peak periods

Choose a primary model and fallback

Select the primary model from measured application results, then configure a fallback that accepts the same request shape and produces output your application can validate. Do not treat a fallback as equivalent until it passes the same tests.

  • Prefer a flash-oriented option when latency and throughput dominate
  • Prefer a higher-capability option when complex task quality dominates
  • Use explicit schema validation for structured outputs
  • Re-evaluate when price, capacity, or model versions change

Integrate through TokenAAS

Create a TokenAAS API key in a group containing the approved models, query GET /v1/models, and send the exact returned model ID to the OpenAI-compatible chat endpoint.

  • Base URL: https://tokenaas.ai/v1
  • Model discovery: GET /v1/models
  • Text requests: POST /v1/chat/completions
  • Current status and price: https://tokenaas.ai/model-plaza

Frequently asked questions

Which is better: DeepSeek, Qwen, GLM, or Kimi?

The answer depends on the workload. Compare the same prompts and measure quality, latency, errors, cost, and capacity instead of relying on one general ranking.

Can I switch between these models without changing authentication?

Yes when the models are configured for the same TokenAAS API key group and compatible endpoint. Change the public model ID and validate the response contract.

Why can a model appear in the catalog but not in GET /v1/models?

It may be Coming soon, disabled, outside the API key's group, or missing eligible routing capacity. GET /v1/models reflects what that key can currently access.

How often should model selection be reviewed?

Review after model-version, pricing, routing, or capacity changes, and whenever production quality or latency moves outside your accepted range.