Qwen3.5 Plus vs Gemini 3.1 Pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Qwen3.5 Plus by Alibaba and Gemini 3.1 Pro by Google 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, Qwen3.5 Plus holds a notable pricing advantage for token ingestion. On output generation, Qwen3.5 Plus is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.1 Pro provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.1 Pro 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.
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Qwen3.5 Plus
Multimodal PlusCommercial cloud multimodal workhorse offering a 1M token context window, deep vision understanding, and robust agentic tool handling.
$0.60 / 1M
$2.40 / 1M
1M tokens
64%
Gemini 3.1 Pro
Agentic CodingReasoning-first developer flagship built for agentic software engineering, repository refactoring, and scientific proofing.
$2.00 / 1M
$12.00 / 1M
1.05M tokens
74.5%
Key Differences at a Glance
- Qwen3.5 Plus is 4.6x cheaper for API usage (input + output tokens combined).
- Gemini 3.1 Pro has a larger context window (1M vs 1M tokens).
- Gemini 3.1 Pro is faster at 110 tok/s vs 85 tok/s.
- Gemini 3.1 Pro leads on SWE-bench coding (74.5% vs 64.0%).
| Feature | Qwen3.5 Plus | Gemini 3.1 Pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 86 |
Provider | Alibaba | |
Input Price Cost per million input tokens | $0.60 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $12.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.40 / 1M | $1.95 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 16K | 66K |
Throughput Speed Tokens generated per second | 85 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 64% | 74.5% |
GPQA Diamond Graduate-level reasoning | 72% | 86.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.50 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.5 Plus · Strengths & Key Advantages
- Massive 1M token context window with full image and video frame ingestion
- Competitive coding and reasoning scores nearing OpenAI and Anthropic flagships
- Outstanding economic value at $0.60 per million input tokens
Considerations & Trade-offs
- Audio modalities not natively supported on the Plus branch
- Can exhibit slight response latency under heavy concurrent batch loads
Gemini 3.1 Pro · Strengths & Key Advantages
- Top-tier SWE-bench Verified coding performance
- Deep Chain-of-Thought with self-correction
- Seamless repository-wide reasoning across 1M tokens
Considerations & Trade-offs
- Premium pricing tier compared to Flash
- Moderate inference latency on difficult problems
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.5 Plus | Gemini 3.1 Pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.19 | $1.85 |
Output Tokens / Task Tokens generated per task | 24K | 52K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 26K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.5 Plus | Gemini 3.1 Pro |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-plus-instruct | models/gemini-3-1-pro |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-02 | 2026-02 |
| Knowledge Cutoff | December 2025 | November 2025 |
| Architecture | Enterprise Million-Token Multimodal Transformer | Pathways High-Capacity Reasoning Architecture |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.6/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Implicit + Explicit Context Caching at $0.5/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Alibaba Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision, audio, video |
| Output Modalities | text | text |
Qwen3.5 Plus vs Gemini 3.1 Pro: Quick Answers
Which is more intelligent, Qwen3.5 Plus or Gemini 3.1 Pro?
Qwen3.5 Plus is more intelligent. It scores 87 compared with 86 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Pro is faster, generating 110 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Qwen3.5 Plus is cheaper at $0.40 per 1M tokens blended, compared with $1.95 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.5 Plus has lower latency, with a time to first token of 0.49s compared with 0.78s.
Which model has a larger context window?
Gemini 3.1 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Qwen3.5 Plus if your priority is Multimodal enterprise document analysis, long-context codebases, and autonomous web agents. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.1 Pro if your workflow prioritizes Autonomous coding bots, deep mathematical proofs, and complex repository refactoring. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Qwen3.5 Plus compare to Gemini 3.1 Pro in API token pricing and cost per token?+
Qwen3.5 Plus costs $0.60 per 1M input tokens and $2.40 per 1M output tokens, whereas Gemini 3.1 Pro costs $2.00 input and $12.00 output. Overall, Qwen3.5 Plus is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen3.5 Plus or Gemini 3.1 Pro?+
Qwen3.5 Plus supports a context window of 1,000K tokens (~750k words), while Gemini 3.1 Pro supports 1,049K tokens (~786k words). Therefore, Gemini 3.1 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3.5 Plus or Gemini 3.1 Pro?+
Qwen3.5 Plus scores 64% on SWE-bench Verified while Gemini 3.1 Pro scores 74.5%. For autonomous software engineering and multi-file debugging, Gemini 3.1 Pro holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3.5 Plus and Gemini 3.1 Pro?+
Qwen3.5 Plus features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.1 Pro provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3.5 Plus and Gemini 3.1 Pro?+
Qwen3.5 Plus generates approximately 85 tokens per second, while Gemini 3.1 Pro delivers 110 tokens per second. In latency-sensitive production environments, Gemini 3.1 Pro provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.5 Plus and Gemini 3.1 Pro to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.5 Plus to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.1 Pro. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs