venice-text-routing

Venice Text-Model Routing

This skill encodes the decision logic for "which Venice text model do I call?" — the routing layer that sits above venice-chat (the call surface) and consumes venice-models (the discovery API).

Primary use case: a local agent receives a prompt, decides whether the local model can handle it, and — if not — picks the cheapest Venice model that satisfies the privacy / modality / capability requirements.

Snapshot freshness

Before applying the matrix below, read snapshots/text-routing.json. If the file is missing, or snapshot_date is older than 30 days, run python scripts/refresh_routing.py to regenerate it from GET /models?type=text + GET /models/traits?type=text. Otherwise trust the cached file — do not hit /models on every routing decision.

refresh_routing.py requires VENICE_API_KEY in the environment and rewrites both snapshots/text-routing.json and routing-matrix.md. Run it once on first install, then ~monthly (CI nightly is also fine).

When to load this skill

• Picking a Venice text model from a prompt at runtime.
• Building a local-first agent that escalates to Venice for hard prompts.
• Deciding privacy tier (anonymized vs private vs TEE vs E2EE).
• Choosing between a trait shortcut (default_reasoning, most_intelligent, …) and a hand-filtered candidate.

For the chat call surface itself, see venice-chat. For raw discovery of every model field, see venice-models.

Privacy tier ladder

Pick the least restrictive tier that satisfies the request — restricting tier shrinks the candidate pool and often raises cost.

Tier	Selector	Guarantee	Use when
Anonymized	model_spec.privacy: "anonymized" (no tee- / e2ee- prefix)	Venice strips IPs / metadata; the underlying provider (Anthropic, xAI, OpenAI, …) sees the prompt under their privacy policy	Default for non-sensitive workloads; only path to certain frontier closed-weights models.
Private	model_spec.privacy: "private" (no prefix)	Open-weights model self-hosted by Venice; zero data retention; no third party sees the prompt	Default for any user data, business logic, or anything you wouldn't paste into ChatGPT.
TEE	model id starts with tee-	Runs inside Intel TDX / NVIDIA confidential-compute enclave; Venice infrastructure cannot read the prompt; verifiable via GET /api/v1/tee/attestation	Regulated data, signed responses required, "Venice itself can't see this."
E2EE	model id starts with e2ee-	TEE + client-side ECDH (secp256k1) / HKDF-SHA256 / AES-256-GCM; prompt encrypted before leaving the device	Strongest. Healthcare, legal, secrets, anything where the wire payload must be opaque. Requires E2EE handshake — see venice-chat.

Sources: docs.venice.ai/overview/privacy, docs.venice.ai/guides/features/tee-e2ee-models.

E2EE is not supported on /responses — route encrypted requests to /chat/completions.

Capability filters

Map prompt requirement → model_spec.capabilities flag (full list in venice-models).

Requirement	Filter	Notes
Vision (single image)	supportsVision: true	Single-image vision models drop older images on each turn — chain images into the last user message.
Vision (multiple images)	supportsVision && supportsMultipleImages	Honor maxImages per request.
Audio input	supportsAudioInput: true	Audio must be base64; URLs are not accepted.
Video input	supportsVideoInput: true	Accepts public URLs (incl. YouTube on some providers) or base64.
Reasoning / chain-of-thought	supportsReasoning: true	If you need to dial effort, additionally require supportsReasoningEffort so reasoning.effort is honored.
Tools / function calling	supportsFunctionCalling: true	Required for any agent loop.
Code-heavy task	optimizedForCode: true	?type=code filter on /models returns only this subset.
Web search (Venice / Brave)	supportsWebSearch: true	Toggle with venice_parameters.enable_web_search.
X / Twitter search	supportsXSearch: true	xAI native (Grok models). Adds ~$0.01/search.
Structured JSON output	supportsResponseSchema: true	Use response_format: {type: "json_schema", ...}.
Large context (≥ 100K tokens)	availableContextTokens >= 100000	Pair with prompt_cache_key + cache_input pricing.
Logprobs	supportsLogProbs: true	Niche — eval / sampling debug.

Cost tiers

The Venice text catalog clusters by per-1M-token price. Buckets below match the size labels documented at docs.venice.ai/models/text. Pick the smallest bucket that hosts a model satisfying your capability filters.

Tier	Rough $/1M in	Rough $/1M out	Use when
XS	< $0.20	< $0.40	Classification, intent extraction, simple summarization.
S	$0.20 – $1	$0.40 – $2	General chat, basic agents, light vision.
M	$1 – $4	$2 – $10	Reasoning at moderate depth, strong code, multi-image vision.
L	$4 – $10	$10 – $30	Long context (≥ 200K), heavy reasoning, complex tool use.
Frontier	≥ $10	≥ $30	Best-available — Claude Opus, GPT-5.x Pro, GLM 5.1 thinking, Grok-4 Heavy. Resolve via trait most_intelligent.

Authoritative per-model pricing lives on model_spec.pricing in snapshots/text-routing.json — never hard-code dollar figures from this prose.

Routing decision tree

Walk top-down. Stop at the first rule that applies.

1. Local-first check
   - Prompt is ≤ ~500 tokens, no special-capability requirement,
     no privacy escalation, no tool calls expected
     → handle on the local model. Do not call Venice.

2. Privacy gate
   - User flagged "private" OR prompt contains regulated data (PHI, secrets, legal):
       require model_spec.privacy === "private"
       OR model id startsWith "tee-" / "e2ee-".
   - User flagged "E2EE" / "must be encrypted in transit":
       require model id startsWith "e2ee-". Skip /responses.
   - User flagged "TEE" / "verifiable inference":
       require model id startsWith "tee-".
   - Otherwise: any tier acceptable (still prefer privacy: "private" when otherwise tied).

3. Modality gate
   - Image input present  → require supportsVision (+ supportsMultipleImages if > 1).
   - Audio input present  → require supportsAudioInput.
   - Video input present  → require supportsVideoInput.

4. Capability gate
   - Tool calls expected               → require supportsFunctionCalling.
   - Code-heavy task                   → prefer optimizedForCode (or call /models?type=code).
   - Chain-of-thought / planning / hard math
                                       → require supportsReasoning;
                                         prefer supportsReasoningEffort to dial reasoning.effort.
   - Structured JSON output required   → require supportsResponseSchema.
   - Web search needed                 → require supportsWebSearch
                                         (or supportsXSearch for X/Twitter content).

5. Context size gate
   - Estimated (prompt + expected output) > availableContextTokens of the candidate
     → bump up to a model with sufficient context. Prefer ones with cache_input pricing.

6. Frontier override
   - User asked for "best", "frontier", "most intelligent", "smartest"
     → resolve trait `most_intelligent` from the snapshot. Skip cost-min step.

7. Cost minimization
   - From surviving candidates, pick the smallest cost tier (XS → S → M → L → frontier).
     Tie-break by lower output $/1M, then lower input $/1M.

8. Sanity filters (apply throughout)
   - Drop model_spec.beta === true unless your key has beta access.
   - Drop model_spec.offline === true.
   - Drop candidates whose model_spec.regionRestrictions exclude the caller.

Trait shortcuts

When the prompt maps cleanly to a named trait, skip the matrix and resolve the trait from the snapshot's traits block (sourced from GET /models/traits?type=text):

Trait	Use for
default	Generic chat / catch-all.
fastest	Latency-critical, low-stakes.
default_reasoning	"Think step by step" without specifying a model.
default_code	Code generation / refactor / review.
default_vision	Vision input, no other special needs.
function_calling_default	Agent loops, tool use.
most_intelligent	"Best available", frontier override.
most_uncensored	Refusal-free / red-team / creative writing without content filtering.

Cache the resolved trait → ID map at session start (one HTTP call) and reuse.

Local-first pattern

For a local agent driving Venice as an "escalation backend":

1. Score the prompt cheaply (locally):

   • Token count of prompt + expected output.
   • Modality signals (any image / audio / video parts).
   • Keyword signals: "code", "reason", "step by step", "private", "secret", "encrypt", "best", "frontier".
   • User-provided overrides (e.g. --model frontier, --privacy e2ee).

2. Decide local vs Venice:

   • If estimated tokens ≤ local model's comfort window AND no capability or privacy escalation → stay local.
   • Otherwise → continue to step 3.

3. Run the decision tree above to pick a Venice model.

4. Call via venice-chat:

   curl https://api.venice.ai/api/v1/chat/completions \
     -H "Authorization: Bearer $VENICE_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "<chosen id>",
       "messages": [...],
       "venice_parameters": {"include_venice_system_prompt": false}
     }'
   

5. Cap blast radius: log the chosen model + estimated cost before sending; refuse to escalate beyond the user's --max-cost ceiling.

Examples

Example 1 — local-first wins

Prompt: "Summarize this email in one sentence: …" (~200 tokens)

• Step 1 → local handles it. Do not call Venice.

Example 2 — privacy + reasoning

Prompt: "Here's our customer churn dataset (PII). Reason about which factors drive churn."

• Step 2 → privacy gate requires privacy: "private" or tee- / e2ee- prefix.
• Step 4 → reasoning required → supportsReasoning: true.
• Step 7 → among private/TEE candidates with reasoning, pick the cheapest.
• → typically a tee-* reasoning model, or traits.default_reasoning filtered to privacy: "private".

Example 3 — multi-image vision

Prompt: 3 product photos + "Compare these for build quality."

• Step 3 → supportsVision && supportsMultipleImages && maxImages >= 3.
• Step 7 → cheapest survivor → typically traits.default_vision.

Example 4 — frontier intelligence on a long doc

Prompt: "Give me your absolute best take on this 50-page legal brief."

• Step 5 → context size gate triggers (> 100K tokens).
• Step 6 → frontier override fires (most_intelligent).
• → resolve traits.most_intelligent (a Claude Opus / GPT-5.x Pro / GLM 5.1 family ID, depending on the snapshot).

Example 5 — code agent with tools

Prompt: "Refactor this repo. You have shell + edit tools."

• Step 4 → supportsFunctionCalling && optimizedForCode.
• Step 7 → cheapest survivor → traits.default_code if it also supports tools, else traits.function_calling_default filtered by optimizedForCode.

Future extensions

A scripts/route.py CLI may be added later for runtimes that prefer structured output (route.py --prompt '...' --max-cost 0.001 --need-vision → {"model_id": "...", "estimated_cost": ..., "tier": "..."}). The prose decision tree above remains the source of truth.

Sibling routing skills (venice-image-routing, venice-audio-routing, venice-video-routing) can mirror this layout when needed.

Gotchas

• Don't hard-code model IDs. Venice rolls models monthly — resolve via traits or filters against the snapshot.
• Stale snapshot lies silently. The 30-day rule is the floor; refresh sooner if you hit a 404 on a model ID.
• Privacy ≠ uncensored. most_uncensored and privacy: "private" are independent axes.
• enable_e2ee defaults to true on E2EE-capable models when the right headers are present — see venice-chat. The routing decision selects the model; the chat skill drives the handshake.
• Beta / offline models in the snapshot. Filter out model_spec.beta === true (unless your key has beta access) and model_spec.offline === true before scoring.
• Region restrictions. model_spec.regionRestrictions[] returns 403 outside the listed countries — drop those candidates if your caller is outside.
• Trait keys differ by type. Always pass ?type=text. Don't reuse image traits.

See also

• venice-models — /models, /models/traits, /models/compatibility_mapping (the discovery API this skill consumes).
• venice-chat — /chat/completions (the call surface this skill picks a model for).
• venice-auth — Bearer vs x402 wallet auth.
• venice-billing — confirming actual spend matched the routing estimate.
• venice-errors — 402 / 422 / 429 handling on routed requests.
• Per-model snapshot: snapshots/text-routing.json.
• Per-model human-readable matrix: routing-matrix.md.