GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GLM-4.5 by Zhipu AI and Qwen3-Next 80B-A3B (Thinking) 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 131K tokens. In latency-sensitive production environments, Qwen3-Next 80B-A3B (Thinking) 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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GLM-4.5
Hybrid ReasoningZhipu flagship open-source release introducing hybrid deliberate reasoning, deep mathematical derivation, and agentic workflows.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
42%
Qwen3-Next 80B-A3B (Thinking)
Efficient ThinkerHigh-efficiency open reasoning MoE model that unleashes deep chain-of-thought exploration while utilizing only 3B active parameters per token.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
46%
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3-Next 80B-A3B (Thinking) is faster at 95 tok/s vs 75 tok/s.
- Qwen3-Next 80B-A3B (Thinking) leads on SWE-bench coding (46.0% vs 42.0%).
| Feature | GLM-4.5 | Qwen3-Next 80B-A3B (Thinking) |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 82 |
Provider | Zhipu AI | 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 | 131.1K tokens~197 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 75 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 42% | 46% |
GPQA Diamond Graduate-level reasoning | 61% | 68.5% |
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.
GLM-4.5 · Strengths & Key Advantages
- Strong hybrid reasoning switching smoothly between fast chat and deliberate logic
- Permissive open-weights release with straightforward deployment documentation
- High accuracy on bilingual Chinese-English enterprise prompts
Considerations & Trade-offs
- Superseded by later GLM-4.7 and GLM-5 releases for large-scale software engineering
- Context window limited to 131K tokens
Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages
- Remarkable STEM and coding reasoning performance from just 3B active parameters
- High throughput compared to traditional dense 70B+ reasoners
- Open weights with permissive commercial license
Considerations & Trade-offs
- Extended reasoning traces increase generation latency and memory cache usage
- Can overthink trivial conversational queries
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.5 | Qwen3-Next 80B-A3B (Thinking) |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 16K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 12K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.5 | Qwen3-Next 80B-A3B (Thinking) |
|---|---|---|
| Official API Model ID (SDK) | glm-4-5 | qwen3-next-80b-a3b-thinking-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-07 | 2025-09 |
| Knowledge Cutoff | May 2025 | August 2025 |
| Architecture | Hybrid Reasoning Mixture-of-Experts Transformer | Deliberate Reasoning Sparse Mixture-of-Experts |
| Parameters | 130B MoE | 80B total (3B 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 | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 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 | text |
| Output Modalities | text | text |
GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): Quick Answers
Which is more intelligent, GLM-4.5 or Qwen3-Next 80B-A3B (Thinking)?
Qwen3-Next 80B-A3B (Thinking) is more intelligent. It scores 82 compared with 80 on the composite Intelligence Index.
Which model is faster?
Qwen3-Next 80B-A3B (Thinking) is faster, generating 95 tokens per second compared with 75 tokens per second.
Which model has lower latency?
Qwen3-Next 80B-A3B (Thinking) has lower latency, with a time to first token of 0.54s compared with 0.55s.
Verdict
Verdict & Model Selection Recommendation: Choose GLM-4.5 if your priority is On-premises enterprise conversational agents, bilingual reasoning, and data analysis. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3-Next 80B-A3B (Thinking) if your workflow prioritizes Local hardware reasoning, scientific problem-solving, and on-premises math tutoring. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does GLM-4.5 compare to Qwen3-Next 80B-A3B (Thinking) in API token pricing and cost per token?+
Both GLM-4.5 and Qwen3-Next 80B-A3B (Thinking) 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: GLM-4.5 or Qwen3-Next 80B-A3B (Thinking)?+
Both GLM-4.5 and Qwen3-Next 80B-A3B (Thinking) offer an identical context window of 131K tokens (~98k 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: GLM-4.5 or Qwen3-Next 80B-A3B (Thinking)?+
GLM-4.5 scores 42% on SWE-bench Verified while Qwen3-Next 80B-A3B (Thinking) scores 46%. For autonomous software engineering and multi-file debugging, Qwen3-Next 80B-A3B (Thinking) holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GLM-4.5 and Qwen3-Next 80B-A3B (Thinking)?+
GLM-4.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3-Next 80B-A3B (Thinking) provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-4.5 and Qwen3-Next 80B-A3B (Thinking)?+
GLM-4.5 generates approximately 75 tokens per second, while Qwen3-Next 80B-A3B (Thinking) delivers 95 tokens per second. In latency-sensitive production environments, Qwen3-Next 80B-A3B (Thinking) provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-4.5 and Qwen3-Next 80B-A3B (Thinking) 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-Next 80B-A3B (Thinking), and route deep architectural or complex reasoning queries to GLM-4.5. 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.
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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