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Support guide

Best AI models for customer support

This page prioritizes models that can handle long support threads, structured replies, and agent workflows while keeping cost under control for high-volume traffic.

Live signalLow enough pricing for repeated support traffic and retries
Live signalStructured output support for predictable summaries and ticket payloads
Live signalTool support for CRM, help desk, and action workflows
Live signalEnough context for long customer threads and policy prompts
Fast answer

Start with the live shortlist, then validate the route

Support traffic usually needs low cost, reliable formatting, and enough context to preserve the full conversation. Tool support also matters when the model needs to look up or update systems.

Data source and freshness

Catalog-backed, not a static price sheet

TVP refreshes this page from the live OpenRouter model catalog. This render used 428 public model records and was synchronized Jul 14, 2026, 9:34 PM UTC. Pricing, availability, and context values can change.

Verify the exact route before sending production traffic, then use the linked model and provider pages as the source for current values. Read the TVP data methodology.

Shortlist

Top live candidates right now

inclusionai

inclusionAI: Ling-2.6-flash

Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for real-world agents that require fast responses, strong execution, and high token efficiency....

Context262,144
Input$0.01
Output$0.03

$0.01 input and $0.03 output, structured workflows, and enough context make this practical for support traffic.

  • text
  • tools
  • structured
Model detailsProvider page
meta-llama

Meta: Llama 3.1 8B Instruct

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...

Context131,072
Input$0.02
Output$0.03

$0.02 input and $0.03 output, structured workflows, and enough context make this practical for support traffic.

  • text
  • tools
  • structured
Model detailsProvider page
mistralai

Mistral: Mistral Nemo

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...

Context131,072
Input$0.02
Output$0.03

$0.02 input and $0.03 output, structured workflows, and enough context make this practical for support traffic.

  • text
  • tools
  • structured
Model detailsProvider page
nex-agi

Nex AGI: Nex-N2-Mini

Nex-N2-Mini is an open-source agentic mixture-of-experts model from Nex AGI, the smaller sibling in the Nex-N2 series. It accepts text and image input and is built for coding, tool use,...

Context262,144
Input$0.025
Output$0.1

$0.025 input and $0.1 output, structured workflows, and enough context make this practical for support traffic.

  • text
  • image
  • tools
  • structured
Model detailsProvider page
qwen

Qwen: Qwen2.5 7B Instruct

Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...

Context131,072
Input$0.04
Output$0.1

$0.04 input and $0.1 output, structured workflows, and enough context make this practical for support traffic.

  • text
  • tools
  • structured
Model detailsProvider page
FAQ

What buyers usually ask

What matters most for support models?

Support traffic usually needs low cost, reliable formatting, and enough context to preserve the full conversation. Tool support also matters when the model needs to look up or update systems.

Should support teams use frontier models by default?

Only when the issue complexity justifies it. Most support routes benefit more from stable lower-cost models with structured output and tools than from the most expensive reasoning tier.

Next step

Use the guide, then validate the route in live TVP data.

TVP keeps the shortlist connected to the current catalog, provider coverage, and token pricing so buyers can move from research to routing without starting over.

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