Model selection

By default every call runs on the model Anona serves as standard. You can name a different one (per request, in the dashboard, or as a default for a space), and credits are charged at that model’s own rate rather than a flat tier.

Not to be confused with memory models, which are standing answers a space keeps current. This page is about the LLM that does the work.

Every model is available on every plan

There is no plan gate. A Developer account can select the most expensive model in the catalog, and a Scale account pays the same rate for it. That works because credits are derived from each model’s real per-token cost. A credit balance is therefore a spend cap, and the choice of model cannot move it. It only changes how fast the balance goes down. Spent entirely on input, the same 5,000 credits buy roughly 10.7M tokens of nova-micro, 2.5M of gpt-oss, or 68K of claude-opus-4.6, and cost us the same either way. The practical limit is throughput, not entitlement: request-per-minute quotas are per model, and the premium models carry the least headroom. See requests_per_minute in the table below.

The default

Omit model and you get openai.gpt-oss-120b-1:0, also reachable as the tier name balanced. It replaced Nova Pro as the default in September 2026: same extraction quality on our own benchmark, a fifth of the credits per operation, and twenty times the requests-per-minute headroom. Nova Pro is still selectable by name as nova-pro.

Pick one, and copy the call

Take a name out of the rate table below and drop it into the model field. Nothing else about the call changes:

curl https://api.anonalabs.com/v1/reason \
  -H "Authorization: Bearer $ANONA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "space_id": "support",
    "query": "what does this customer keep asking for?",
    "model": "gpt-oss"
  }'

"model": "gpt-oss" is the default, so that call is also what you get by leaving the field out: 2 credits per 1K input, 8 per 1K output, 1,000 requests a minute. Swap the one string for any other name in the table — nova-lite for a fifth of the rate, claude-opus-4.6 for about forty times it — and the credit rate moves with it. The same field works on the chat proxy, and on PUT /v1/spaces/{id}/reason-settings to make it the space default.

Three ways to name a model

A short name, like nova-pro, deepseek or glm, is what you will use most. One per model, and pinned to that model permanently: a short name is never re-pointed at something else. Most short names carry no version, because most families have one member here. Where a family has two, both names carry the version: claude-sonnet-4.5 and claude-sonnet-4.6, claude-opus-4.5 and claude-opus-4.6. That is the same promise, not an exception to it. A bare claude-sonnet would have to mean one of them today and could only mean the other later, which is exactly the substitution short names exist to prevent. A tier name, like fast or balanced, names an intent rather than a model, and Anona keeps it pointed at a sensible choice as the landscape changes. That re-pointing is the value: ask for fast and you keep getting whatever “fast” should mean. A full model id, like us.amazon.nova-pro-v1:0, also works, and always has. Nothing that already sends one needs to change. GET /v1/models returns the full id for every model, along with its exact credit rates. A host-qualified name, like cerebras/gpt-oss-120b, is accepted for any model served outside Bedrock. It is the provider/model form other tools use, and it is worth knowing because two entries here can be the same open weights at different hosts: gpt-oss and gpt-oss-turbo are both GPT-OSS 120B, and gpt-oss-turbo charges 2.33x on input and 1.25x on output, which is 2.02x blended. If you are ever unsure which one a name refers to, spell the host out.

The difference between the first two matters. A short name is stable; a tier name may move. If you have measured a model and want that exact behaviour to stay put, name it: gpt-oss, not balanced.

{ "space_id": "support", "query": "what does this customer keep asking for?", "model": "nova-lite" }

These all name the same model, and any of them is accepted anywhere model is:

{ "model": "gpt-oss" }
{ "model": "balanced" }
{ "model": "openai.gpt-oss-120b-1:0" }

And these three all name the Cerebras-hosted one, which is a different model and a different price:

{ "model": "gpt-oss-turbo" }
{ "model": "cerebras.gpt-oss-120b" }
{ "model": "cerebras/gpt-oss-120b" }

Models served through OpenRouter are named openrouter/<vendor>/<model>. This one is GPT-OSS 120B again, at a third host and its own, lower, price:

{ "model": "gpt-oss-or" }
{ "model": "openrouter/openai/gpt-oss-120b" }

The vendor name on its own (openai/gpt-oss-120b) is not accepted, because more than one host serves those weights and the price depends on which. The first two agree today. balanced is an intent that may be re-pointed later, and in September 2026 it moved from Nova Pro to GPT-OSS; gpt-oss is that model permanently. If you had pinned nova-pro, you are still on Nova Pro.

Using it from the API

Resolution runs request → space default → platform default, so a request always wins over a stored setting.

# Per request (reason)
curl -X POST https://api.anonalabs.com/v1/reason \
  -H "Authorization: Bearer anona_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"space_id": "support", "query": "...", "model": "balanced"}'

# Space default (reason)
curl -X PUT https://api.anonalabs.com/v1/spaces/support/reason-settings \
  -H "Authorization: Bearer anona_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "fast", "depth": null}'

# Space default (proxy)
curl -X PUT https://api.anonalabs.com/v1/spaces/support/chat-settings \
  -H "Authorization: Bearer anona_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"chat_model": "fast"}'

from anona import AnonaClient

anona = AnonaClient(api_key="anona_live_YOUR_KEY")

# Per request (reason)
answer = anona.reason(space_id="support", query="...", model="balanced")

# Space default (reason) — both fields, always: the PUT is a full replace
anona.set_reason_settings(space_id="support", model="fast", depth=None)

# Space default (proxy)
anona.set_chat_settings(space_id="support", chat_model="fast")

import { Anona } from "@anona-labs/memory";

const anona = new Anona({ apiKey: "anona_live_YOUR_KEY" });

// Per request (reason)
const answer = await anona.reason({
  spaceId: "support",
  query: "...",
  model: "balanced",
});

// Space default (reason) — both fields, always: the PUT is a full replace
await anona.setReasonSettings({ spaceId: "support", model: "fast", depth: null });

// Space default (proxy)
await anona.setChatSettings({ spaceId: "support", chatModel: "fast" });

Both settings PUTs replace the whole record, which is why the SDK methods send every field: an argument you leave out is an argument that clears. Because the proxy is drop-in, a stock OpenAI SDK can set it once and forget it:

from openai import OpenAI

client = OpenAI(
    api_key="anona_live_YOUR_KEY",
    base_url="https://api.anonalabs.com/v1",
    default_headers={"X-Anona-Space-Id": "support"},
)

resp = client.chat.completions.create(
    model="fast",                      # a tier name or a model id
    messages=[{"role": "user", "content": "What did we decide about pricing?"}],
)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "anona_live_YOUR_KEY",
  baseURL: "https://api.anonalabs.com/v1",
  defaultHeaders: { "X-Anona-Space-Id": "support" },
});

const resp = await client.chat.completions.create({
  model: "fast", // a tier name or a model id
  messages: [{ role: "user", content: "What did we decide about pricing?" }],
});

curl https://api.anonalabs.com/v1/chat/completions \
  -H "Authorization: Bearer anona_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Anona-Space-Id: support" \
  -d '{
    "model": "fast",
    "messages": [{"role": "user", "content": "What did we decide about pricing?"}]
  }'

A space default is stored as a resolved model id, even when you send a tier name. If fast is later re-pointed at a different model, spaces that already chose it stay where they are. You picked a behaviour, not a subscription to ours. GET /v1/spaces/{space_id}/reason-settings reads the current value; sending {"model": null}, or DELETE, returns the space to the platform default. That resource holds a second setting beside the model — depth, how hard reason looks before it answers — and the PUT replaces the whole record, so a body naming only model clears the depth. Send both fields, which is why the example above states "depth": null rather than leaving it out. The reason model also writes the space’s memory models. A memory model is a reason answer the space keeps, so POST /v1/spaces/{id}/models and POST /v1/spaces/{id}/models/{id}/refresh run on the same model /v1/reason does, and so does a refresh that consolidation starts on its own. Change the reason model and the next refresh picks it up — existing content is not rewritten until then.

PUT /v1/spaces/{id}/chat-settings replaces the whole record, so a field you omit is cleared, not kept. The reason settings are a separate resource for exactly this reason, so saving chat defaults never disturbs them — but that PUT replaces its whole record too, and it holds both model and depth.

Using it from the dashboard

Open a space, then the chat defaults tab:

Row	What it changes
Chat model	The LLM that answers proxied chat calls (`/v1/chat/completions`, `/v1/responses`, `/v1/messages`) for this space.
Reason model	The LLM that answers `/v1/reason` for this space — and the one that writes its memory models.
Reason depth	How hard `reason` looks before it answers here: Fast lets a current memory model answer on its own, Thorough always reads the notes and raw memories underneath. See Depth.

Each row has an override toggle. Left off, the row is inherited: the space follows the platform default, and it keeps following it if that default ever moves. Turn the toggle on and the picker appears, listing every model with its credit rate per 1K tokens so the cost of the choice is visible at the moment you make it. These write the same reason-settings and chat-settings resources the API uses, so a value set here is the value a request sees, and vice versa.

List what you can use

GET /v1/models
Authorization: Bearer anona_live_YOUR_KEY

curl https://api.anonalabs.com/v1/models \
  -H "Authorization: Bearer anona_live_YOUR_KEY"

catalog = anona.list_catalog_models()
for model in catalog["data"]:
    print(model["id"], model["credits_per_1k_input"])

const catalog = await anona.listCatalogModels();
for (const model of catalog.data) {
  console.log(model.id, model.credits_per_1k_input);
}

{
  "object": "list",
  "data": [
    {
      "id": "us.amazon.nova-lite-v1:0",
      "object": "model",
      "created": 0,
      "owned_by": "anona",
      "short_name": "nova-lite",
      "display_name": "Nova Lite",
      "alias": "fast",
      "default": false,
      "credits_per_1k_input": 0.8,
      "credits_per_1k_output": 3.2,
      "requests_per_minute": 400,
      "relative_cost": 0.4,
      "provider": "bedrock",
      "supports_memory_operations": true
    }
  ]
}

The envelope is the standard OpenAI list shape, so client.models.list() works unchanged against the proxy. Entries come back cheapest first, ordered by relative_cost. The names, alias, default, the credit rates, requests_per_minute, and relative_cost are Anona’s additions; an SDK that does not know about them ignores them. short_name always names this exact model and never moves; alias names a tier and may be re-pointed. default marks the model you get by omitting the model field. At most one entry carries it, and sometimes none, when this deployment serves a model outside the catalog.

Where each model works

Most models answer everything. A few answer chat only, and GET /v1/models says which with supports_memory_operations — read it rather than assuming, and filter on it before offering a list as a reason model. The reason is the shape of the product rather than a restriction on you. Chat requests are proxied: Anona calls the model’s host directly, so any host we hold a key for can answer one. Memory operations — /v1/reason, the memory models, the Slack and MCP asks — run inside the memory engine, which talks to one host. A model outside it is refused with unsupported_model and a message naming what is available, rather than accepted and failed later. provider names the host. It matters because the same open weights can be served by more than one, at different prices and very different speeds: gpt-oss and gpt-oss-turbo are the same 120B model, the second on Cerebras at roughly 2x the credits and, measured over tool-calling calls of the shape reason sends, several times faster per step. Anona holds every one of these keys; there is no bring-your-own-key path, and a model you can name is one we pay for.

What it costs

Credit rates are derived from each model’s real per-token price, so a model that costs five times more charges roughly five times the credits, with no rounding into tiers. Cost vs default compares against gpt-oss, blended 10:1 input:output. It is a shopping aid, not a billing figure: a model’s in/out ratio is its own. Llama 70B charges the same for both, GPT-OSS charges 4x more for output, so cost a real workload from the exact credit rates on GET /v1/models, not from the multiplier.

Model	Use in `model`	Tier	Credits per 1K (in / out)	Cost vs default
GPT-OSS 20B (OpenRouter)	`gpt-oss-20b-or`	-	0.25 / 1.27	0.14x
DeepSeek V4 Flash 0423 (OpenRouter)	`deepseek-v4-flash-or`	-	0.36 / 0.71	0.15x
Nova Micro	`nova-micro`	-	0.47 / 1.87	0.23x
Nova Micro 1.0 (OpenRouter)	`nova-micro-v1-or`	-	0.49 / 1.97	0.25x
GPT-OSS 120B (OpenRouter)	`gpt-oss-or`	-	0.52 / 2.63	0.28x
Llama 3.1 8B Instruct (OpenRouter)	`llama-3.1-8b-instruct-or`	-	0.7 / 1.13	0.29x
Gemma 4 26B A4B (OpenRouter)	`gemma-4-26b-a4b-it-or`	-	0.59 / 3.09	0.32x
Nemotron Nano 3 30B	`nemotron-nano`	-	0.8 / 3.2	0.40x
Nova Lite	`nova-lite`	`fast`	0.8 / 3.2	0.40x
Nova Lite 1.0 (OpenRouter)	`nova-lite-v1-or`	-	0.84 / 3.38	0.42x
Nemotron 3 Nano 30B A3B (OpenRouter)	`nemotron-3-nano-30b-a3b-or`	-	0.84 / 3.38	0.42x
Nemotron 3.5 Lightning (OpenRouter)	`nemotron-3.5-lightning-or`	-	0.98 / 2.81	0.45x
GPT-OSS 20B	`gpt-oss-20b`	-	0.93 / 4	0.48x
GLM 4.7 Flash	`glm-flash`	-	0.93 / 5.33	0.52x
DeepSeek V4.1 Flash (OpenRouter)	`deepseek-v4.1-flash-or`	-	0.63 / 8.44	0.53x
GLM 4.7 Flash (OpenRouter)	`glm-4.7-flash-or`	-	0.98 / 5.63	0.55x
GLM 5.3 Flash (OpenRouter)	`glm-5.3-flash-or`	-	1.27 / 4.22	0.60x
Ministral 3 3B 2512 (OpenRouter)	`ministral-3b-2512-or`	-	1.55 / 1.55	0.61x
Nemotron 3 Super (OpenRouter)	`nemotron-3-super-120b-a12b-or`	-	1.2 / 6.33	0.65x
Llama 3.3 70B Instruct (OpenRouter)	`llama-3.3-70b-instruct-or`	-	1.41 / 4.5	0.66x
GPT-6 Luna Pro (OpenRouter)	`gpt-6-luna-pro-or`	-	1.55 / 7.74	0.83x
GPT-6 Luna (OpenRouter)	`gpt-6-luna-or`	-	1.55 / 7.74	0.83x
MiMo-V2.6-Flash (OpenRouter)	`mimo-v2.6-flash-or`	-	1.97 / 3.94	0.84x
Ministral 3 8B 2512 (OpenRouter)	`ministral-8b-2512-or`	-	2.32 / 2.32	0.91x
GLM 5.2 (OpenRouter)	`glm-5.2-or`	-	1.99 / 6.25	0.93x
Qwen3.8 Flash (OpenRouter)	`qwen3.8-flash-or`	-	2.11 / 6.61	0.99x
Hy3 (OpenRouter)	`hy3-or`	-	1.97 / 8.16	0.99x
GPT-OSS 120B	`gpt-oss`	`balanced`	2 / 8	1x, the default
Nemotron 3 Super 120B	`nemotron-super`	-	2 / 8.67	1.0x
GLM 5.3 (OpenRouter)	`glm-5.3-or`	-	2.19 / 6.87	1.0x
Llama 4 Scout	`llama4-scout`	-	2.27 / 8.8	1.1x
Mistral Small 4 (OpenRouter)	`mistral-small-2603-or`	-	2.32 / 9.28	1.2x
Ministral 3 14B 2512 (OpenRouter)	`ministral-14b-2512-or`	-	3.09 / 3.09	1.2x
DeepSeek V4 Pro 0423 (OpenRouter)	`deepseek-v4-pro-or`	-	2.94 / 5.87	1.3x
Llama 4 Maverick (OpenRouter)	`llama-4-maverick-or`	-	2.64 / 9.18	1.3x
MiniMax M2.7 (OpenRouter)	`minimax-m2.7-or`	-	2.95 / 11.82	1.5x
DeepSeek V3.2 (OpenRouter)	`deepseek-v3.2-or`	-	3.66 / 5.35	1.5x
GPT-5.6 Luna Pro (OpenRouter)	`gpt-5.6-luna-pro-or`	-	3.09 / 18.57	1.8x
GPT-5.6 Luna (OpenRouter)	`gpt-5.6-luna-or`	-	3.09 / 18.57	1.8x
DeepSeek V3.1 (OpenRouter)	`deepseek-chat-v3.1-or`	-	3.8 / 14.07	1.9x
Qwen3.8 27B (OpenRouter)	`qwen3.8-27b-or`	-	2.85 / 26.37	2.0x
MiniMax M2.1	`minimax`	-	4 / 16	2.0x
DeepSeek V3.1 Terminus (OpenRouter)	`deepseek-v3.1-terminus-or`	-	4.22 / 14.07	2.0x
GPT-OSS 120B (Cerebras)	`gpt-oss-turbo`	-	4.67 / 10	2.0x
MiniMax M2.5 (OpenRouter)	`minimax-m2.5-or`	-	4.15 / 16.88	2.1x
MiniMax M2.1 (OpenRouter)	`minimax-m2.1-or`	-	4.22 / 16.88	2.1x
Gemini 3.1 Flash Lite (OpenRouter)	`gemini-3.1-flash-lite-or`	-	3.87 / 23.21	2.2x
Command A+ (OpenRouter)	`command-a-plus-or`	-	4.22 / 21.1	2.3x
GLM 5.3 FlashX (OpenRouter)	`glm-5.3-flashx-or`	-	5.2 / 17.58	2.5x
MiMo-V2.6-Pro (OpenRouter)	`mimo-v2.6-pro-or`	-	6.12 / 12.24	2.6x
Nova 2 Lite (OpenRouter)	`nova-2-lite-v1-or`	-	4.22 / 35.17	2.8x
Gemini 3.5 Flash Lite (OpenRouter)	`gemini-3.5-flash-lite-or`	-	4.64 / 38.68	3.0x
Mistral Large 3	`mistral-large`	-	6.67 / 20	3.1x
Mistral Medium 3.1 (OpenRouter)	`mistral-medium-3.1-or`	-	6.19 / 30.95	3.3x
Llama 70B	`llama-70b`	-	9.6 / 9.6	3.8x
DeepSeek	`deepseek`	-	8.27 / 24.67	3.8x
GLM	`glm`	-	8 / 29.33	3.9x
GLM 4.7 (OpenRouter)	`glm-4.7-or`	-	8.44 / 30.95	4.1x
Kimi K2.6 (OpenRouter)	`kimi-k2.6-or`	-	8.42 / 35.45	4.3x
Kimi K2.5	`kimi`	-	8 / 40	4.3x
GLM 5 (OpenRouter)	`glm-5-or`	-	9.85 / 31.51	4.6x
Kimi K2.5 (OpenRouter)	`kimi-k2.5-or`	-	8.44 / 46.77	4.7x
Nemotron 3 Ultra (OpenRouter)	`nemotron-3-ultra-550b-a55b-or`	-	8.79 / 43.96	4.7x
Nova Pro	`nova-pro`	-	10.67 / 42.67	5.3x
Nova Pro 1.0 (OpenRouter)	`nova-pro-v1-or`	-	11.25 / 45.01	5.6x
Gemini 3.8 Flash (OpenRouter)	`gemini-3.8-flash-or`	-	10.55 / 52.75	5.7x
Gemini 3.7 Flash (OpenRouter)	`gemini-3.7-flash-or`	-	10.55 / 52.75	5.7x
Gemini 3.6 Flash (OpenRouter)	`gemini-3.6-flash-or`	-	11.6 / 58.02	6.2x
GLM 5	`glm-5`	-	13.33 / 42.67	6.3x
Grok 4.3 (OpenRouter)	`grok-4.3-or`	-	17.58 / 35.17	7.5x
Claude Haiku 4.5	`claude-haiku`	-	14.67 / 73.33	7.9x
Claude Haiku 4.5 (OpenRouter)	`claude-haiku-4.5-or`	-	15.47 / 77.37	8.3x
Mistral Medium 3.5 (OpenRouter)	`mistral-medium-3-5-or`	-	23.21 / 116.05	12.4x
Qwen3.8 2.4T A95B (OpenRouter)	`qwen3.8-2.4t-a95b-or`	-	28.13 / 84.4	13.1x
Grok 4.5 (OpenRouter)	`grok-4.5-or`	-	28.13 / 84.4	13.1x
Gemini 3.5 Flash (OpenRouter)	`gemini-3.5-flash-or`	-	23.21 / 139.26	13.3x
Grok 4.7 (OpenRouter)	`grok-4.7-or`	-	30.95 / 92.84	14.4x
Grok 4.6 (OpenRouter)	`grok-4.6-or`	-	30.95 / 92.84	14.4x
Claude Sonnet 5.5 (OpenRouter)	`claude-sonnet-5.5-or`	-	28.13 / 140.67	15.1x
Claude Sonnet 5 (OpenRouter)	`claude-sonnet-5-or`	-	28.13 / 140.67	15.1x
GPT-5.6 Sol Pro (OpenRouter)	`gpt-5.6-sol-pro-or`	-	28.13 / 140.67	15.1x
GPT-5.6 Sol (OpenRouter)	`gpt-5.6-sol-or`	-	28.13 / 140.67	15.1x
GPT-6.1 Sol Pro (OpenRouter)	`gpt-6.1-sol-pro-or`	-	30.95 / 154.73	16.6x
GPT-6.1 Sol (OpenRouter)	`gpt-6.1-sol-or`	-	30.95 / 154.73	16.6x
GPT-6 Sol Pro (OpenRouter)	`gpt-6-sol-pro-or`	-	30.95 / 154.73	16.6x
GPT-6 Sol (OpenRouter)	`gpt-6-sol-or`	-	30.95 / 154.73	16.6x
GPT-5.6 Terra Pro (OpenRouter)	`gpt-5.6-terra-pro-or`	-	30.95 / 185.68	17.7x
GPT-5.6 Terra (OpenRouter)	`gpt-5.6-terra-or`	-	30.95 / 185.68	17.7x
Kimi K3 (OpenRouter)	`kimi-k3-or`	-	31.65 / 196.93	18.3x
GLM 5.3 Prime (OpenRouter)	`glm-5.3-prime-or`	-	39.39 / 123.79	18.5x
Claude Sonnet 4.5 (OpenRouter)	`claude-sonnet-4.5-or`	-	42.2 / 211	22.6x
Claude Sonnet 4.6 (OpenRouter)	`claude-sonnet-4.6-or`	-	42.2 / 211	22.6x
Claude Sonnet 4.5	`claude-sonnet-4.5`	-	44 / 220	23.6x
Claude Sonnet 4.6	`claude-sonnet-4.6`	-	44 / 220	23.6x
Qwen3.8 Max Prime (OpenRouter)	`qwen3.8-max-prime-or`	-	56.27 / 168.8	26.1x
Claude Opus 5.5 (OpenRouter)	`claude-opus-5.5-or`	-	56.27 / 281.33	30.1x
Claude Opus 4.5 (OpenRouter)	`claude-opus-4.5-or`	-	70.33 / 351.67	37.7x
Claude Opus 4.6 (OpenRouter)	`claude-opus-4.6-or`	-	70.33 / 351.67	37.7x
Claude Opus 5 (OpenRouter)	`claude-opus-5-or`	-	70.33 / 351.67	37.7x
Claude Opus 4.8 (OpenRouter)	`claude-opus-4.8-or`	-	70.33 / 351.67	37.7x
Claude Opus 4.7 (OpenRouter)	`claude-opus-4.7-or`	-	70.33 / 351.67	37.7x
Claude Opus 4.5	`claude-opus-4.5`	-	73.33 / 366.67	39.3x
Claude Opus 4.6	`claude-opus-4.6`	-	73.33 / 366.67	39.3x
GPT-5.5 (OpenRouter)	`gpt-5.5-or`	-	77.37 / 464.2	44.2x
Claude Fable 5.1 (OpenRouter)	`claude-fable-5.1-or`	-	140.67 / 703.33	75.4x
Claude Fable 5 (OpenRouter)	`claude-fable-5-or`	-	140.67 / 703.33	75.4x
GPT-6 Astra (OpenRouter)	`gpt-6-astra-or`	-	154.73 / 773.67	82.9x
GPT-6 Astra Pro (OpenRouter)	`gpt-6-astra-pro-or`	-	154.73 / 773.67	82.9x

GET /v1/models is the authoritative list; this table is a snapshot. The two can differ in length as well as in content: a model whose host this deployment holds no key for is not listed, though it stays resolvable by id so naming it gets you a precise model_unavailable rather than a flat unsupported_model. If a name in this table is missing from your GET /v1/models, that is why. Reason also carries a flat base of 5 credits, so a short answer on a cheap model costs 5 rather than fractions of one.

A worked example. A reason call reading 4,000 tokens of context and writing 400 on the default costs 5 + (4 × 2.0) + (0.4 × 8.0) ≈ 16 credits. The same call on nova-micro costs 5 + (4 × 0.47) + (0.4 × 1.87) ≈ 8 credits, and at that end of the catalog the flat base is most of the bill, not the tokens.

Which model answered

Reason echoes it back, so you can reconcile an answer and its cost against the model that actually produced them rather than the one you asked for:

{
  "insights": "...",
  "model": "us.amazon.nova-lite-v1:0",
  "usage": { "input_tokens": 4120, "output_tokens": 380 }
}

Errors

Anona never quietly substitutes a different model. If the one you named cannot serve the request, the request fails and says so.

Code	Status	Meaning
`unsupported_model`	400	Not a model this deployment serves — or, on a memory operation, a chat-only one (see `supports_memory_operations`). The message lists what is.
`model_throttled`	429	That model is at capacity right now. Retry, or use one with more headroom (see `requests_per_minute`).
`model_unavailable`	503	The model is listed but this deployment cannot reach it. Ours to fix.

Request limits are per model, which is also a reason to choose one: the high-throughput models carry forty times the headroom of the premium ones (1,000 rpm against Opus’s 25).

Choosing well

Leave it alone unless you have a reason. The default is the balanced option.
fast for high-volume, low-stakes work: classification, short recall summaries, anything where you would rather have ten answers than one better one.
A premium model for reason over a large space, where the synthesis is the product and a better one is worth several times the credits. mistral-large is 3.1x the default and kimi 4.3x; the Claude family runs from claude-haiku at 7.9x to claude-opus-4.6 at 39.3x. Price is identical within each Claude family, so prefer the higher version: it carries a larger context window for the same rate.
Watch the credit burn, not the plan. Nothing stops a Developer account running any model in the catalog; it simply exhausts the month’s credits sooner. claude-opus-4.6 burns about 170 times faster than nova-micro, and the dearest entry in the catalog about 355 times faster. Both figures are the ratio of the Cost vs default column below, so they carry its 10:1 blend.
gpt-oss-turbo is cheapest exactly where it is fastest. It is the same model as the default at a faster host. It charges 2.33x on input and only 1.25x on output, so what you pay depends on the shape of the call — and so does what you gain. Measured against prod with alternating calls:
call credits speed
short reply (~50 tokens out) the same 3 1.24x faster
long answer (~700 tokens out) 6 → 8, 1.33x 3.64x faster
large context in, short answer out up to 2.2x barely faster
So: worth it for drafting, summarising and anything that writes at length, where it is over three times faster for a third more credits. Not worth it for stuffing a large context in to get a sentence back — that is where it costs most and helps least. A short interactive reply is free either way, because both land on the 3-credit chat floor. Choose it per call rather than as a space default.
Measure before pinning an id. Reason is a multi-step agent loop, so a slower model costs more wall time than its per-call latency suggests.

Getting Started

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Changelog

Every model is available on every plan

The default

Pick one, and copy the call

Three ways to name a model

Using it from the API

Using it from the dashboard

List what you can use

Where each model works

What it costs

Which model answered

Errors

Choosing well

​Every model is available on every plan

​The default

​Pick one, and copy the call

​Three ways to name a model

​Using it from the API

​Using it from the dashboard

​List what you can use

​Where each model works

​What it costs

​Which model answered

​Errors

​Choosing well

Every model is available on every plan

The default

Pick one, and copy the call

Three ways to name a model

Using it from the API

Using it from the dashboard

List what you can use

Where each model works

What it costs

Which model answered

Errors

Choosing well