Qwen/Qwen3.5-9B
alibaba/qwen3-5-9bPar Alibaba (Qwen) · famille: qwen · sorti 2026-03-03 · fin de connaissance: 2025-04
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacités
Model fit scores
0–100 · higher is betterThese 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.
Coding64
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)8/20
- Provider availability6/10
Agents71
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability6/10
JSON / structured output50
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency79
- Headline price (log-scaled)79/95
- Has prompt-cache pricing0/5
Long context61
- Context window (100K → 2M)51/90
- Has published price for full window10/10
Vision87
- Accepts image input50/50
- Context window (10K → 1M)21/30
- Has published price10/10
- Provider availability6/10
Production-readiness84
- Number of independent providers30/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
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.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.33 < $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 | $0.65 < $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 | $0.17 < $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 | $0.55 < $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 | $0.69 < $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 kilo · qwen/qwen3.5-9b
Disponible chez 6 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| SiliconFlow (China) siliconflow-cn | Qwen/Qwen3.5-9B | $0.220 | $1.74 | 262K | 2026-03-03 |
| Kilo Gateway kilo | qwen/qwen3.5-9b | $0.050 | $0.150 | 256K | 2026-03-10 |
| Mixlayer mixlayer | qwen/qwen3.5-9b | $0.100 | $0.400 | 262K | 2026-03-18 |
| Venice AI venice | qwen3-5-9b | $0.100 | $0.150 | 256K | 2026-03-05 |
| OVHcloud AI Endpoints ovhcloud | qwen3.5-9b | $0.100 | $0.150 | 262K | 2026-02-15 |
| Regolo AI regolo-ai | qwen3.5-9b | $0.150 | $0.600 | 262K | 2026-02-01 |
Incohérences de données entre fournisseurs
- context_window varies: 256000, 262144
- release_date varies (span 45d): 2026-02-01, 2026-02-15, 2026-03-03, 2026-03-05, 2026-03-10, 2026-03-18
- modalities varies across offerings
Les fournisseurs rapportent des valeurs différentes pour ce modèle. Les infos clés ci-dessus utilisent un fournisseur représentatif ; voir le tableau pour le détail par fournisseur.
Frequently asked questions
How much does Qwen/Qwen3.5-9B cost?
Qwen/Qwen3.5-9B costs $0.050 per 1M input tokens and $0.150 per 1M output tokens, sourced from kilo. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Qwen/Qwen3.5-9B?
Qwen/Qwen3.5-9B 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 Qwen/Qwen3.5-9B support tool calling?
Yes. Qwen/Qwen3.5-9B 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 Qwen/Qwen3.5-9B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Qwen/Qwen3.5-9B in our data source. Verify with Alibaba (Qwen)'s official documentation before relying on it in production.
Can Qwen/Qwen3.5-9B accept image input?
Yes. Qwen/Qwen3.5-9B accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Alibaba (Qwen)'s docs for the exact rule.
Is Qwen/Qwen3.5-9B open-weight?
Yes. Qwen/Qwen3.5-9B'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 Qwen/Qwen3.5-9B?
If Qwen/Qwen3.5-9B doesn't fit, consider Qwen3.5 397B-A17B, Qwen3 32B, Qwen3 235B A22B Instruct 2507. 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 Alibaba (Qwen) models
- Qwen3.5 397B-A17B$0.60 in / $3.60 out
- Qwen3 32B$0.70 in / $2.80 out
- Qwen3 235B A22B Instruct 2507$0.10 in / $0.10 out
- Qwen3-Coder 480B-A35B Instruct$1.50 in / $7.50 out
- Qwen3-235B-A22B-Thinking-2507$0.10 in / $0.10 out
Capability lists this model is in
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.