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Qwen3.5 397B-A17B

llmgateway/qwen35-397b-a17b

Par llmgateway · famille: qwen · sorti 2026-02-15

⚠ Il s'agit d'un fine-tune communautaire ou dérivé — pas d'une publication officielle de l'éditeur.

$0.600
Entrée / 1M jetons
$3.60
Sortie / 1M jetons
262K
Fenêtre de contexte
66K
Sortie max

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

Capacités

✓ Tool calling✓ Raisonnement✓ Sortie structurée✓ Pièces jointes✓ Poids ouverts✓ Contrôle de température
Modalités: entrée text, image, video, audio · sortie text

Model fit scores

0–100 · higher is better

These scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.

Coding79
  • Tool calling40/40
  • Structured output20/20
  • Reasoning10/10
  • Context window (100K → 1M)8/20
  • Provider availability1/10
Agents91
  • Tool calling35/35
  • Structured output25/25
  • Reasoning15/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output92
  • Structured output / JSON mode50/50
  • Tool calling20/20
  • Temperature control10/10
  • Price-friendly for high-volume12/20
Cost efficiency47
  • Headline price (log-scaled)47/95
  • Has prompt-cache pricing0/5
Long context61
  • Context window (100K → 2M)51/90
  • Has published price for full window10/10
Vision82
  • Accepts image input50/50
  • Context window (10K → 1M)21/30
  • Has published price10/10
  • Provider availability1/10
Production-readiness50
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K15/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)0/15

Cost Efficiency Index

Open full calculator →

Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.

ScenarioCostAssumption
RAG answer
per 1,000 RAG answers
$4.80
< $0.01 per request
5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot.
Support ticket triage
per 10,000 tickets
$9.60
< $0.01 per request
1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support.
Data extraction
per 1,000 documents
$3.00
< $0.01 per request
2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines.
Code review
per 1,000 PRs
$8.40
< $0.01 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$9.36
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

Détail des tarifs

Tarif recommandé de llmgateway · qwen35-397b-a17b

$0.600
Entrée
$3.60
Sortie

Disponible chez 1 fournisseurs

FournisseurID modèle fournisseurEntrée / 1MSortie / 1MContextePublié le
LLM Gateway
llmgateway
qwen35-397b-a17b$0.600$3.60262K2026-02-15

Frequently asked questions

How much does Qwen3.5 397B-A17B cost?

Qwen3.5 397B-A17B costs $0.600 per 1M input tokens and $3.60 per 1M output tokens, sourced from llmgateway. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of Qwen3.5 397B-A17B?

Qwen3.5 397B-A17B has a context window of 262K tokens, with a max output of 66K tokens per reply. This is the total combined size of prompt + completion.

Does Qwen3.5 397B-A17B support tool calling?

Yes. Qwen3.5 397B-A17B supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Does Qwen3.5 397B-A17B support structured output / JSON mode?

Yes. Qwen3.5 397B-A17B supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Can Qwen3.5 397B-A17B accept image input?

Yes. Qwen3.5 397B-A17B accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check llmgateway's docs for the exact rule.

Is Qwen3.5 397B-A17B open-weight?

Yes. Qwen3.5 397B-A17B's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.

What are the best alternatives to Qwen3.5 397B-A17B?

If Qwen3.5 397B-A17B doesn't fit, consider Auto Route, Seed 1.6 Flash (250715), Seed 1.6 (250615). Each one targets the same use case — see the Related links below for direct head-to-head pages.

Where does this data come from?

All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.

Explore more

More llmgateway models

Capability lists this model is in

See also

Dernière mise à jour :

Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.