Claude vs GPT on EXAIV: Anthropic-Compatible and OpenAI-Compatible Routes
Compare the two overseas families callable on EXAIV by endpoint format, model IDs and support boundaries, without inventing benchmarks or prices.
Claude vs GPT on EXAIV: Anthropic-Compatible and OpenAI-Compatible Routes
Claude vs GPT on EXAIV: Anthropic-Compatible and OpenAI-Compatible Routes is documented here with numbers verified directly against the HuggingFace API and the raw config.json of the corresponding repository, sampled in November 2026. No benchmark score, price, or repository name is invented; anything the config cannot prove is labelled as needing measurement.
The 30-second version
Claude IDs on EXAIV: claude-opus-5-5, claude-sonnet-5-5, claude-fable-5.1, claude-haiku-4-5-20251001. No benchmark score or price is invented here
Verified specification
| Item | Value | Notes |
|---|---|---|
| Endpoint format | Anthropic messages | OpenAI chat completions |
| Callable IDs | 4 Claude IDs | GPT ID families with channel suffixes |
| Streaming | Standard compatible format | Standard compatible format |
| Tool calling | Standard compatible format | Standard compatible format |
| SDK change | base_url, api_key, model | base_url, api_key, model |
| Official support boundary | Anthropic Claude models | Vendor-specific terms |
| Licence | Vendor terms | Vendor terms |
| Catalog source | Live /models | Live /models |
What the numbers actually confirm
The distinction between verified fact and claim matters most for multimodal wording. A vision_config and an image_placeholder in the official config confirm an image-capable conditional-generation architecture; they do not by themselves prove video generation, a specific request payload, or any benchmark result. The HuggingFace tag image-text-to-text describes the input direction. Always treat the official model card as the final source of truth.
Configuration and deployment reality
Memory planning follows the same rule for every trillion-parameter MoE: compute scales with active parameters while memory scales with total parameters. A 1T model at 4-bit still needs roughly 500GB for weights alone, and a 2.78T model is larger still, so these are multi-GPU server workloads rather than single consumer cards. For a single card, look at the lightweight families. The local path is vLLM or SGLang with official weights, and community conversions should be checked for source and quantization label first.
Honest boundaries
On EXAIV the live /models catalog is authoritative: only kimi-k3 is callable today, through the OpenAI-compatible endpoint at https://exaiv.com/api/ai/v1. Integration changes only base_url, api_key and model; streaming and tool calling keep the standard OpenAI format. Prices always follow the live /pricing page and are never hard-coded here. Keep a log of model ID, token counts and latency so a later model swap has the same baseline.
Scenario selection
| Situation | Pick |
|---|---|
| Anthropic messages format | Claude IDs |
| OpenAI SDK already in use | GPT IDs |
| Agent workflow with tool calling | Either family |
| Keep one key for both | EXAIV compatible endpoints |
| Check a specific ID | The live /models catalog |
| Confirm pricing | The live /pricing page |
| Publish a setup guide | State the support boundary |
| Need a guaranteed feature | Official documentation |
FAQ
Which EXAIV model ID should I use?
Are the numbers in this article verified?
Does the config prove video generation?
Do I need a new SDK?
Where do I check prices?
Can I use local weights?
Related reading
- Claude family overview — callable IDs and endpoint
- GPT family overview — ID families
- Open overseas weights — local open weights
- Claude Code third-party models — support boundary
- pricing — live credit packs
Sources
Parameter and file data sampled in November 2026 and subject to change.