Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 397B-A17B by Alibaba and Qwen3.5 122B-A10B by Alibaba represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 262K tokens. In latency-sensitive production environments, Qwen3.5 122B-A10B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Changez et Comparez les Modèles
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen3.5 397B-A17B
Grand MoE open sourcemassif ouvert multimodal MoE modèle supporting joint vision-langue raisonnement avec un ultra-efficient 17B actif paramètre empreinte.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
58%
Qwen3.5 122B-A10B
VLM open sourceéquilibré ouvert multimodal MoE modèle développé pour élevé-débit visuel compréhension avec seulement 10B actif paramètres.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Différences Clés en un Coup d’Œil
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3.5 122B-A10B is faster at 78 tok/s vs 64 tok/s.
| Caractéristique | Qwen3.5 397B-A17B | Qwen3.5 122B-A10B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 82 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 64 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 58% | N/A |
GPQA Diamond Graduate-level reasoning | 70% | 65.5% |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.5 397B-A17B · Strengths & Key Advantages
- Unites natif vision traitement avec de pointe-classe codage et mathématique raisonnement
- Remarkably faible inférence coût pour un nearly 400B modèle due à 17B actif routage
- solide open source alternative à propriétaire multimodal de pointe systems
Considerations & Trade-offs
- nécessite substantiel unified mémoire matériel (minimum 4x A100/H100 80GB)
- Quantized variants peut show slight degradation sur precise UI coordinate prediction
Qwen3.5 122B-A10B · Strengths & Key Advantages
- Feasible à deploy sur dual-GPU systems (2x RTX 6000 Ada ou 2x A100)
- solide visuel ancrage et multitour document parsing
- à poids ouverts avec comprehensive appel d'outils et JSON mode
Considerations & Trade-offs
- inférieur theoretical profondeur que le 397B phare sibling
- Occasional minor formatage slips sur deeply nested JSON schemas
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.5 397B-A17B | Qwen3.5 122B-A10B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 22K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 5K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Qwen3.5 397B-A17B | Qwen3.5 122B-A10B |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-397b-a17b-instruct | qwen3-5-122b-a10b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-02 | 2026-02 |
| Knowledge Cutoff | December 2025 | December 2025 |
| Architecture | Next-Gen Multimodal Sparse Mixture-of-Experts | Efficient Open Multimodal MoE Transformer |
| Parameters | 397B total (17B active MoE) | 122B total (10B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 246 GB VRAM (4-bit Quantized) · 854 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 262 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B: Réponses Rapides (FAQ)
Which is more intelligent, Qwen3.5 397B-A17B or Qwen3.5 122B-A10B?
Qwen3.5 397B-A17B is more intelligent. It scores 86 compared with 82 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 122B-A10B is faster, generating 78 tokens per second compared with 64 tokens per second.
Which model has lower latency?
Qwen3.5 122B-A10B has lower latency, with a time to first token of 0.51s compared with 0.65s.
Verdict
Verdict & Model Selection Recommendation: Choose Qwen3.5 397B-A17B if your priority is Self-hosted multimodal agents, enterprise technical document processing, and private research. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.5 122B-A10B if your workflow prioritizes Mid-sized on-premise multimodal deployment and automated document intelligence. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Qwen3.5 397B-A17B compare to Qwen3.5 122B-A10B in API token pricing and cost per token?+
Both Qwen3.5 397B-A17B and Qwen3.5 122B-A10B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Qwen3.5 397B-A17B or Qwen3.5 122B-A10B?+
Both Qwen3.5 397B-A17B and Qwen3.5 122B-A10B offer an identical context window of 262K tokens (~197k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Qwen3.5 397B-A17B or Qwen3.5 122B-A10B?+
Qwen3.5 397B-A17B scores 58% on SWE-bench Verified, while Qwen3.5 122B-A10B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen3.5 397B-A17B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3.5 397B-A17B and Qwen3.5 122B-A10B?+
Qwen3.5 397B-A17B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.5 122B-A10B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3.5 397B-A17B and Qwen3.5 122B-A10B?+
Qwen3.5 397B-A17B generates approximately 64 tokens per second, while Qwen3.5 122B-A10B delivers 78 tokens per second. In latency-sensitive production environments, Qwen3.5 122B-A10B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.5 397B-A17B and Qwen3.5 122B-A10B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Qwen3.5 122B-A10B, and route deep architectural or complex reasoning queries to Qwen3.5 397B-A17B. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- GLM-4.7-FlashX vs Qwen3.5 122B-A10B: Benchmarks, Pricing & Speed ComparisonComparer
- Qwen3.5 122B-A10B vs Qwen3-Next 80B-A3B (Thinking): Benchmarks, Pricing & Speed ComparisonComparer
- Qwen3.5 397B-A17B vs Llama 5: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonComparer
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs