Purpose

A detailed performance comparison between GLM-4.7-Flash and Qwen3-Coder-Next, focusing on Token Per Second (inference speed) and coding quality metrics. These two models represent leading open-source options for local coding assistant deployments in 2026.

Token Per Second (Throughput) Performance

GLM-4.7-Flash

Local Deployment Performance:

  • Consumer GPUs (RTX 3090/4090): 60–80+ tokens/second
  • Mac M-series chips: 60–80+ tokens/second

Performance at Different Context Sizes (RTX 3090):

  • 4K context: Prompt processing ~2000 t/s, generation ~93 t/s
  • 16K context: Prompt processing ~1000 t/s, generation ~63 t/s
  • 32K context: Prompt processing ~620 t/s, generation ~43 t/s

Specialized Hardware:

  • Cerebras hardware: ~1,000 t/s (up to 1,700 t/s for certain use cases)

Qwen3-Coder-Next

Cloud API Providers:

  • Novita (FP8): 133.6 t/s (fastest)
  • Together.ai (FP8): 74.9 t/s
  • Standard performance: ~100 t/s

Local Deployment Performance:

  • CPU offload (8GB VRAM + 32GB RAM): ~12 t/s
  • Typical local inference: Significantly slower than cloud providers due to model size (80B parameters, though only 3B active)

Key Finding: Token Per Second Comparison

For cloud API deployment, Qwen3-Coder-Next has a significant throughput advantage (100–133.6 t/s vs 60–80 t/s for GLM-4.7-Flash local).

For local deployment on consumer hardware, GLM-4.7-Flash offers comparable or better performance (60–80 t/s) compared to Qwen3-Coder-Next’s CPU-only option (12 t/s).

Coding Quality Benchmarks

SWE-Bench Results (Software Engineering Benchmark)

ModelSWE-Bench VerifiedSWE-Bench MultilingualSWE-Bench Pro
GLM-4.7-Flash59.2%N/AN/A
Qwen3-Coder-Next70.6%62.8%44.3%

Analysis: Qwen3-Coder-Next significantly outperforms GLM-4.7-Flash on SWE-Bench Verified (70.6% vs 59.2%), suggesting superior performance on real-world software engineering tasks.

Specialized Security Benchmarks

BenchmarkQwen3-Coder-NextComparison
SecCodeBench61.2%Exceeds Claude Opus 4.5 (52.5%)
CWEval (func-sec@1)56.32%Leading score
Terminal-Bench 2.036.2%N/A
Aider66.2%N/A

Analysis: Qwen3-Coder-Next shows exceptional security-focused code generation, particularly outperforming Claude Opus 4.5 on SecCodeBench.

General Reasoning & Math Benchmarks

BenchmarkGLM-4.7-Flash
AIME 2025 (Advanced Math)91.6%
GPQA (Graduate Reasoning)75.2%
τ²-Bench (Tool Use)79.5%

Analysis: GLM-4.7-Flash demonstrates strong general reasoning capabilities alongside coding, approaching Claude 3.5 Sonnet levels on tool invocation.

Architecture Comparison

GLM-4.7-Flash

  • Total Parameters: 30 billion
  • Active Parameters: ~3 billion (MoE architecture)
  • Context Length: 128,000 tokens
  • Release Date: January 19, 2026
  • Model Class: 30B-A3B Mixture of Experts

Qwen3-Coder-Next

  • Total Parameters: 80 billion
  • Active Parameters: ~3 billion (ultra-sparse MoE)
  • Designed For: Coding agents and local development
  • Release Date: February 2026
  • Training: 800K+ verifiable tasks with executable environments
  • Special Capabilities: Tool use, code execution, runtime error recovery

Summary: When to Use Each

Choose GLM-4.7-Flash If:

  • You need fastest local deployment on consumer GPUs
  • General reasoning + coding balance matters
  • You value simplicity with 30B total parameters
  • You need high throughput on limited hardware (RTX 3090/4090)
  • Math reasoning and tool use are important (AIME 91.6%, τ²-Bench 79.5%)

Choose Qwen3-Coder-Next If:

  • SWE-Bench performance is critical (70.6% vs 59.2%)
  • Security-focused code generation matters (SecCodeBench 61.2%)
  • You have access to cloud API deployment (100–133.6 t/s)
  • You need specialized coding agent capabilities (tool use, code execution, error recovery)
  • Multi-language code is required (SWE-Bench Multilingual 62.8%)

Conclusion

For Throughput: Qwen3-Coder-Next wins on cloud APIs (100–133.6 t/s), while GLM-4.7-Flash wins for local consumer hardware deployment (60–80 t/s).

For Code Quality: Qwen3-Coder-Next demonstrates superior performance on standard SWE-Bench benchmarks (70.6% vs 59.2%), with particularly strong results on security-focused code generation.

Overall Trade-off: GLM-4.7-Flash offers superior efficiency and local deployability with excellent general reasoning, while Qwen3-Coder-Next provides better specialized coding performance at the cost of larger model size and infrastructure requirements.

Sources

  1. GLM-4.7-Flash: The Ultimate 2026 Guide to Local AI Coding Assistant
  2. GLM-4.7-Flash Complete Guide 2026: Free AI Coding Assistant & Agentic Workflows
  3. GLM-4.7-Flash: Release Date, Free Tier & Key Features (2026)
  4. Qwen3-Coder-Next: The Complete 2026 Guide to Running Powerful AI Coding Agents Locally
  5. Qwen Team Releases Qwen3-Coder-Next: An Open-Weight Language Model
  6. Qwen3-Coder-Next offers vibe coders a powerful open source, ultra-sparse model with 10x higher throughput for repo tasks
  7. Qwen3-Coder-Next - Intelligence, Performance & Price Analysis
  8. Alibaba’s Qwen3-Coder-Next Activates Just 3B of 80B Parameters For Improved Efficiency
  9. GLM-4.7-Flash vs Qwen3-Coder Comparison: Benchmarks
  10. GLM-4.7 Flash Review: High-Performance Coding on a Budget
  11. Qwen3 Coder Performance Evaluation: A Comparative Analysis Against Leading Models
  12. Qwen3-Coder-Next: Pushing Small Hybrid Models