Head-to-Head Model Comparison

Claude Opus 5.5 vs Qwen3.8 Max Preview

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Reviewed by the GetInfoUs research teamLast reviewed: October 2026Editorial policy
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Compare Claude Opus 5.5 by Anthropic against Qwen3.8 Max Preview by Alibaba across real-world coding, reasoning, token economics, and latency.

Head-to-Head Comparison

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Popular Matchups:
Anthropic

Claude Opus 5.5

Frontier Intelligence Titan

Anthropic's most intelligent model for complex reasoning, research, and strategic analysis.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Alibaba

Qwen3.8 Max Preview

2.4T Open Flagship

Epochal 2.4-trillion parameter open-weights flagship combining 1M context with multimodal reasoning and true frontier agent autonomy.

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

Key Differences at a Glance

  • Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 is faster at 78 tok/s vs 60 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 75.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude Opus 5.5Qwen3.8 Max Preview
Intelligence Index
Composite reasoning & knowledge score (0-100)
9493
Provider
AnthropicAlibaba
Input Price
Cost per million input tokens
$4.00 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$20.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$3.08 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K32K
Throughput Speed
Tokens generated per second
78 tok/s60 tok/s
SWE-bench Verified
Real-world software engineering
84.6%75.2%
GPQA Diamond
Graduate-level reasoning
82.3%79.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.40 / 1M$0.00 / 1M

Who Wins Where?

At-a-glance decision framework
Coding
Claude Opus 5.5
+9.4% SWE-bench lead
Cost
Qwen3.8 Max Preview
1.0x cheaper monthly runtime
Speed
Claude Opus 5.5
+30% faster generation
Context
Tied
1M tokens window

Empirical Head-to-Head Benchmark Scores

Official evaluation metrics verified by independent third-party research labs.

Claude Opus 5.5Qwen3.8 Max Preview
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

84.6%vs75.2%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

82.3%vs79.5%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

91.5%vs89%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1507ptsvs1485pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

19.8%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

66.4%vsN/A
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

58.7%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

83.1%vs77%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

95.4%vsN/A
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

92.6%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

78 tok/s vs60 tok/s

Latency & Response Time

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Claude Opus 5.51.40s
Qwen3.8 Max Preview0.68s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude Opus 5.57.81s
Qwen3.8 Max Preview9.01s

Real-Time Interactive Token Cost Simulator

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Qwen3.8 Max Preview Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3.8 Max Preview saves approximately $24.60 / month (100%) under this workload.1.0x Savings

Claude Opus 5.5 · Strengths & Key Advantages

  • Unmatched autonomous multi-step agentic coding and refactoring
  • Flawless multi-document reasoning over 1M token context with no long-prompt surcharge
  • Highest composite Intelligence Index score measured (94/100), leading SWE-bench Verified at 84.6%
  • Precise tool execution with robust error-recovery loops
Considerations & Trade-offs
  • Premium output pricing ($20.00/M) plus heavy reasoning-token usage (~68K output tokens per complex task) keeps long agentic runs expensive
  • Long thinking times at maximum effort can push a single hard agentic task toward ~$3.85+

Qwen3.8 Max Preview · Strengths & Key Advantages

  • World-record open-weights model scale (2.4T total with 95B active inference)
  • Chatbot Arena Elo of 1485 rivaling proprietary closed frontier models
  • True sovereign open model with uncompromised weights and zero censorship
Considerations & Trade-offs
  • Enormous hardware footprint requires enterprise datacenters or specialized cloud hosting
  • Quantized INT4 weights still require over 600GB of distributed VRAM

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricClaude Opus 5.5Qwen3.8 Max Preview
Cost per Task
Estimated USD to finish one task
$3.85$0.00
Output Tokens / Task
Tokens generated per task
68K35K
Reasoning Tokens / Task
Thinking tokens burned per task
42K15K

Technical Specifications

Architecture, release timeline, capabilities, and modalities.

FeatureClaude Opus 5.5Qwen3.8 Max Preview
Official API Model ID (SDK)claude-opus-5-5-20260901qwen3-8-max-preview-instruct
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-092026-07
Knowledge CutoffJune 2026April 2026
ArchitectureExtended Autonomous Hybrid Reasoning TransformerColossus-Scale Open Sparse Mixture-of-Experts
ParametersConfidential2.4T total (95B active MoE)
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search Citations + Code Execution Tool + MCP ConnectorsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText 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 VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16)
Safety & Frontier ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Claude Opus 5.5 vs Qwen3.8 Max Preview: Quick Answers

Which is more intelligent, Claude Opus 5.5 or Qwen3.8 Max Preview?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.

Which model is faster?

Claude Opus 5.5 is faster, generating 78 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.8 Max Preview has lower latency, with a time to first token of 0.68s compared with 1.40s.

Verdict

Choose Claude Opus 5.5 for large-scale software development agents, enterprise codebases, and mission-critical scientific synthesis, or choose Qwen3.8 Max Preview for national research centers, sovereign enterprise clouds, and frontier ai research.

Frequently asked questions

Which is cheaper: Claude Opus 5.5 or Qwen3.8 Max Preview?+

Claude Opus 5.5 costs $4/1M input and $20/1M output tokens, while Qwen3.8 Max Preview costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: Claude Opus 5.5 or Qwen3.8 Max Preview?+

Claude Opus 5.5 supports 1M tokens whereas Qwen3.8 Max Preview supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Sources: Anthropic & Alibaba Official Documentation