Claude Sonnet 5.5 vs Qwen3.8 Max
As of November 10, 2026, Claude Sonnet 5.5 and Qwen3.8 Max cost the same per input token ($2.00 per 1M), but Claude Sonnet 5.5 costs 67% more per output token ($10.00 vs $6.00) and has a 1M-token context window versus 992K.
Claude Sonnet 5.5 vs Qwen3.8 Max side by side
| Claude Sonnet 5.5 | Qwen3.8 Max | |
|---|---|---|
| Input, per 1M tokens | $2.00 | $2.00 |
| Output, per 1M tokens | $10.00 | $6.00 |
| Cache read, per 1M tokens | $0.10 | $0.25 |
| Cache write, per 1M tokens | $2.50 | – |
| Context window | 1,000,000 tokens | 991,808 tokens |
| Max output | 128,000 tokens | 131,072 tokens |
| Image input | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output (JSON) | Yes | Yes |
| Prompt caching | Yes | Yes |
| Reasoning | Yes | Yes |
| Providers on LiteLLM | 9 | 12 |
| Price shown is from | Anthropic | Alibaba Cloud Model Studio |
| Added to LiteLLM | September 28, 2026 | August 7, 2026 |
Cheapest provider for each
- Claude Sonnet 5.5: Databricks at $2.00 input and $10.00 output per 1M tokens (
databricks/databricks-claude-sonnet-5-5) - Qwen3.8 Max: DeepInfra at $1.65 input and $4.951 output per 1M tokens (
deepinfra/Qwen/Qwen3.8-Max)
Benchmark scores
| Benchmark | Claude Sonnet 5.5 | Qwen3.8 Max |
|---|---|---|
| Coding and agents | ||
| FrontierSWE | 61.9% (max) | 15.8% (xhigh) |
| APEX-Agents | 75.5% (max) | 63.3% |
| Reasoning and knowledge | ||
| GPQA Diamond | 95.6% (max) | 92.7% (xhigh) |
| SimpleQA Verified | 46.5% (max) | 45.8% (xhigh) |
| Math | ||
| FrontierMath (tiers 1-3) | 88.8% (max) | 74.7% (xhigh) |
| FrontierMath (tier 4) | 80.5% (max) | 46.3% (xhigh) |
| OTIS Mock AIME 2024-2025 | 100.0% (max) | 99.4% (xhigh) |
When to pick which
Pick Qwen3.8 Max for
- Costs 40% less per output token: $6.00 vs $10.00 per 1M.
From differences in the data above only; where the data is equal, no reason is given.
Call Claude Sonnet 5.5 or Qwen3.8 Max with LiteLLM
Both use the OpenAI format through LiteLLM, so switching is a model name change. Set each provider's API key in the environment first.
from litellm import completion
messages = [{"role": "user", "content": "Hello!"}]
# Claude Sonnet 5.5
response = completion(model="claude-sonnet-5-5", messages=messages)
# Qwen3.8 Max
response = completion(model="dashscope/qwen3.8-max", messages=messages)
# Claude Sonnet 5.5, falling back to Qwen3.8 Max if the call fails
response = completion(model="claude-sonnet-5-5", messages=messages, fallbacks=["dashscope/qwen3.8-max"])FAQ
Which is cheaper, Claude Sonnet 5.5 or Qwen3.8 Max?
Qwen3.8 Max is cheaper at 3 input tokens per output token: $3.00 per 1M tokens blended ($2.00 input, $6.00 output), versus $4.00 ($2.00 and $10.00) for Claude Sonnet 5.5, as of November 10, 2026.
Which has the larger context window, Claude Sonnet 5.5 or Qwen3.8 Max?
Claude Sonnet 5.5 has the larger context window: 1,000,000 tokens, versus 991,808 for Qwen3.8 Max.
Can I switch from Claude Sonnet 5.5 to Qwen3.8 Max without changing my code?
Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "claude-sonnet-5-5" to "dashscope/qwen3.8-max", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.