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If you’re already on OpenAI’s API, switching to ByteSpike means changing three things and shipping. The OpenAI SDK works unchanged; the request shape is identical; only the endpoint, key, and model id differ.

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The three changes

- client = OpenAI(api_key="sk-...")
+ client = OpenAI(
+   base_url="https://llm.bytespike.ai/v1",
+   api_key="sk-byts-...",
+ )
That’s it. The rest of your code — tool definitions, structured outputs, streaming, retries — runs untouched.

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Model id mapping

ByteSpike’s catalog uses model ids that mostly match OpenAI’s, plus ids for non-OpenAI providers:
OpenAI id you were usingByteSpike id (drop-in)
gpt-4ogpt-5-4
gpt-4o-minigpt-5-4-mini
gpt-5 (latest)gpt-5-5
gpt-5-nanogpt-5-4-nano
gpt-5-minigpt-5-4-mini
o1-preview / o1gpt-5-4-mini (closest reasoning capability) — pin to a specific id for repeatability
gpt-image-1gpt-image-2
The full live catalog is at GET /v1/models or bytespike.ai/pricing.

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What you gain

OpenAI directByteSpike
Only OpenAI modelsEvery frontier model under one key (Claude, Gemini, DeepSeek, …)
Per-vendor billingOne wallet, one invoice
Per-vendor rate limitsOne rate-limit envelope, set per-key
Failures billFailures don’t bill
Stripe + invoice manualConsole + Stripe handled

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What stays the same

  • OpenAI SDK — Python, TypeScript, Go, every official client. Just change base_url.
  • Request body shape — messages, tools, tool_choice, response_format, stream, etc. — identical.
  • Response shape — choices[].message.content, tool_calls, usage — identical.
  • Streaming protocol — SSE with data: {json} + data: [DONE] — byte-for-byte compatible.
  • Tool use — tools array with function objects + tool_choice — identical.
  • Structured output — response_format: {type: "json_schema", ...} — identical (on GPT-native paths; translated to the model’s native shape on Claude/Gemini routes).

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Concrete examples

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Chat completion

# Was:
from openai import OpenAI
client = OpenAI()
r = client.chat.completions.create(
    model="gpt-5-4",
    messages=[{"role": "user", "content": "ping"}],
)

# Now:
from openai import OpenAI
client = OpenAI(
    base_url="https://llm.bytespike.ai/v1",
    api_key="sk-byts-...",
)
r = client.chat.completions.create(
    model="gpt-5-4",  # same id; or swap to gpt-5-5 / claude-sonnet-4-6 / gemini-3-1-pro
    messages=[{"role": "user", "content": "ping"}],
)

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Tool use

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"]
        }
    }
}]

# Same call shape — works against gpt-5, claude, gemini, etc.
r = client.chat.completions.create(
    model="claude-sonnet-4-6",
    messages=[{"role": "user", "content": "weather in Tokyo?"}],
    tools=tools,
)

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Image generation

# OpenAI's images.generate works against ByteSpike's catalog:
r = client.images.generate(
    model="seedream-4-5",      # or "nano-banana", "gpt-image-2", ...
    prompt="A timelapse of Tokyo at sunset",
    size="1024x1024",
    n=1,
)
print(r.data[0].url)

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Responses API (o-series + GPT-5 + agents)

r = client.responses.create(
    model="gpt-5-5",
    input="Summarize the doc at https://example.com/doc",
    reasoning={"effort": "medium"},
)
Works against every model in the catalog — ByteSpike translates Responses → each model’s native shape under the hood for non-GPT ids.

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Things to double-check

Each ByteSpike key is bound to one routing group. Pick the group that includes the models you’ll call. If you need GPT-5
  • Claude in the same client, either create two keys (one per group) or pick a multi-group key tier. See Models.
Token-count math is identical (OpenAI’s tokenizer for OpenAI models). Prices may differ — usually lower at ByteSpike for non-OpenAI providers, identical for OpenAI passthroughs. Live rates: bytespike.ai/pricing.
OpenAI’s organization and project headers are accepted but ignored — ByteSpike has its own org model. To attribute spend per project, create one key per project with its own quota cap.
ByteSpike doesn’t expose OpenAI’s stateful APIs (/files, /assistants, /vector_stores). If you depend on them, run them direct against OpenAI; chat completions can still be on ByteSpike. The two clients coexist fine.
Same as above — no fine-tuning surface on ByteSpike. Fine-tune direct, deploy direct, do chat through ByteSpike.

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Step-by-step

  1. Sign up at console.bytespike.ai — see Register.
  2. Top up $5+ for a real test — see Top up.
  3. Create a key in the group matching your model needs (default / claude-default / etc).
  4. Change base_url + api_key in one file in your codebase.
  5. Test against gpt-5-4 first (drop-in) to confirm wiring before changing models.
  6. Swap in non-OpenAI ids (claude-sonnet-4-6, gemini-3-1-pro, deepseek-v4-pro) one at a time.
  7. Compare token counts + latency on a representative sample. ByteSpike usually wins on the non-OpenAI ids; OpenAI passthrough is at parity.

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Reverse migration

If you want to move back to OpenAI direct, the same three changes flip the other way. Keep both clients in your codebase if you want A/B routing — the SDK constructor is the only point of divergence.

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Next

Migrate from Anthropic

Same idea, Anthropic side.

Configure your client

Full per-SDK setup details.

/chat/completions reference

Request / response / streaming protocol.

Models

Full catalog you can call after migrating.