Antseed vs Jais 3

Antseed

A buyer needs no account. The CLI creates a wallet key, free models answer at once and paid ones draw on deposited USDC, with seller, tokens and cost returned in response headers. Sellers are unvetted and can read prompts unless TEE-verified, the proxy forwards a client's own credential headers to them, and every package is pre-1.0.

Jais 3

Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code, and no technical report or security contact is published.

Antseed

1. Send a placeholder key such as antseed-p2p. The proxy forwards whatever Authorization or x-api-key header the client sends to the seller
2. Call GET /v1/models first and request a concrete id. The antseed alias works only when the desktop app has a model selected
3. Handle 402 insufficient_deposits by reading suggestedAmount, then fund the address from antseed buyer deposit with USDC on Base
4. Pass --verifiers antseed-verifier --require-verifier to antseed buyer start when prompts are private, so only TEE-verified sellers are routed
5. Read x-antseed-peer-id and x-antseed-estimated-cost-usd on each response, and pin a seller with <peerId>@<model> when the same one must answer

Jais 3

1. Start vLLM or SGLang with --reasoning-parser k2_horizon, and add --tool-call-parser k2_horizon (vLLM also --enable-auto-tool-choice) for tools, or tool calls come back as raw text
2. Pass reasoning_effort (high, medium or low) and tool_call_format inside chat_template_kwargs. Other values return a template error
3. Send each earlier assistant turn back with reasoning_content as a string, even an empty one. A missing or null thinking field makes the template raise an error
4. Send your own system message, or set default_system_prompt to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras
5. Budget at least 32,768 output tokens at high effort, which the cards recommend for every request