Qwen3.5-397B-A17B vs Qwen3.5-122B-A10B Comparison

Comparing Qwen3.5-397B-A17B and Qwen3.5-122B-A10B across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

32 benchmarks

Qwen3.5-397B-A17B outperforms in 30 benchmarks (AA-LCR, BFCL-V4, BrowseComp, BrowseComp-zh, C-Eval, DeepPlanning, Global PIQA, GPQA, HMMT 2025, HMMT25, IFBench, Include, LiveCodeBench v6, LongBench v2, MAXIFE, MMLU-Pro, MMLU-ProX, MMLU-Redux, MMMLU, Multi-Challenge, NOVA-63, PolyMATH, Seal-0, SuperGPQA, SWE-Bench Verified, t2-bench, Terminal-Bench 2.0, VITA-Bench, WideSearch, WMT24++), while Qwen3.5-122B-A10B is better at 2 benchmarks (Humanity's Last Exam, IFEval).

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

Tue Mar 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3.5-122B-A10B costs less

For input processing, Qwen3.5-397B-A17B ($0.60/1M tokens) is 1.5x more expensive than Qwen3.5-122B-A10B ($0.40/1M tokens).

For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 1.1x more expensive than Qwen3.5-122B-A10B ($3.20/1M tokens).

In conclusion, Qwen3.5-397B-A17B is more expensive than Qwen3.5-122B-A10B.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers

Tue Mar 17 2026 • llm-stats.com

Qwen3.5-397B-A17B

Input tokens$0.60

Output tokens$3.60

Best providerNovita

Qwen3.5-122B-A10B

Input tokens$0.40

Output tokens$3.20

Best providerNovita

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Model Size

Parameter count comparison

275.0B diff

Qwen3.5-397B-A17B has 275.0B more parameters than Qwen3.5-122B-A10B, making it 225.4% larger.

Qwen3.5-397B-A17B

397.0Bparameters

Qwen3.5-122B-A10B

122.0Bparameters

397.0B

Qwen3.5-397B-A17B

122.0B

Qwen3.5-122B-A10B

Context Window

Maximum input and output token capacity

Both models have the same input context window of 262,144 tokens. Both models can generate responses up to 64,000 tokens.

Qwen3.5-397B-A17B

Input262,144 tokens

Output64,000 tokens

Qwen3.5-122B-A10B

Input262,144 tokens

Output64,000 tokens

Tue Mar 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.5-397B-A17B and Qwen3.5-122B-A10B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3.5-397B-A17B

Text

Images

Audio

Video

Qwen3.5-122B-A10B

Text

Images

Audio

Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Qwen3.5-397B-A17B

Apache 2.0

Open weights

Qwen3.5-122B-A10B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while Qwen3.5-122B-A10B was released on 2026-02-24.

Qwen3.5-122B-A10B is 0 month newer than Qwen3.5-397B-A17B.

Qwen3.5-397B-A17B

Feb 16, 2026

4 weeks ago

Qwen3.5-122B-A10B

Feb 24, 2026

3 weeks ago

1w newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Qwen3.5-397B-A17B is available from Novita. Qwen3.5-122B-A10B is available from Novita. The availability of providers can affect quality of the model and reliability.

Qwen3.5-397B-A17B

Novita

Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

Qwen3.5-122B-A10B

Novita

Input Price:Input: $0.40/1MOutput Price:Output: $3.20/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Higher AA-LCR score (68.7% vs 66.9%)

Higher BFCL-V4 score (72.9% vs 72.2%)

Higher BrowseComp score (69.0% vs 63.8%)

Higher BrowseComp-zh score (70.3% vs 69.9%)

Higher C-Eval score (93.0% vs 91.9%)

Higher DeepPlanning score (34.3% vs 24.1%)

Higher Global PIQA score (89.8% vs 88.4%)

Higher GPQA score (88.4% vs 86.6%)

Higher HMMT 2025 score (94.8% vs 91.4%)

Higher HMMT25 score (92.7% vs 90.3%)

Higher IFBench score (76.5% vs 76.1%)

Higher Include score (85.6% vs 82.8%)

Higher LiveCodeBench v6 score (83.6% vs 78.9%)

Higher LongBench v2 score (63.2% vs 60.2%)

Higher MAXIFE score (88.2% vs 87.9%)

Higher MMLU-Pro score (87.8% vs 86.7%)

Higher MMLU-ProX score (84.7% vs 82.2%)

Higher MMLU-Redux score (94.9% vs 94.0%)

Higher MMMLU score (88.5% vs 86.7%)

Higher Multi-Challenge score (67.6% vs 61.5%)

Higher NOVA-63 score (59.1% vs 58.6%)

Higher PolyMATH score (73.3% vs 68.9%)

Higher Seal-0 score (46.9% vs 44.1%)

Higher SuperGPQA score (70.4% vs 67.1%)

Higher SWE-Bench Verified score (76.4% vs 72.0%)

Higher t2-bench score (86.7% vs 79.5%)

Higher Terminal-Bench 2.0 score (52.5% vs 49.4%)

Higher VITA-Bench score (49.7% vs 33.6%)

Higher WideSearch score (74.0% vs 60.5%)

Higher WMT24++ score (78.9% vs 78.3%)

Qwen3.5-122B-A10B

View details

Alibaba Cloud / Qwen Team

Less expensive input tokens

Less expensive output tokens

Higher Humanity's Last Exam score (47.5% vs 28.7%)

Higher IFEval score (93.4% vs 92.6%)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen3.5-397B-A17B	Qwen3.5-122B-A10B