GLM-4.7-Flash vs MiniMax-M2.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GLM-4.7-Flash by Zhipu AI and MiniMax-M2.5 by MiniMax 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, MiniMax-M2.5 holds a notable pricing advantage for token ingestion. On output generation, MiniMax-M2.5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, MiniMax-M2.5 provides an expanded memory horizon with 204K tokens. In latency-sensitive production environments, GLM-4.7-Flash 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-4.7-Flash
Budget GLMHigh-speed 200K context API model built for rapid routing, lightweight agents, and cost-effective high-volume API applications.
$0.01 / 1M
$0.03 / 1M
200K tokens
N/A
MiniMax-M2.5
Coding AgentCode-specialized open MoE model engineered for terminal navigation, automated script generation, and office productivity.
$0.00 / 1M
$0.00 / 1M
204K tokens
48%
Key Differences at a Glance
- MiniMax-M2.5 is 1.0x cheaper for API usage (input + output tokens combined).
- MiniMax-M2.5 has a larger context window (204K vs 200K tokens).
- GLM-4.7-Flash is faster at 160 tok/s vs 80 tok/s.
| Feature | GLM-4.7-Flash | MiniMax-M2.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 82 |
Provider | Zhipu AI | MiniMax |
Input Price Cost per million input tokens | $0.01 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.03 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 204K tokens~306 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 160 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 48% |
GPQA Diamond Graduate-level reasoning | N/A | 64% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.00 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical 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.
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-4.7-Flash · Strengths & Key Advantages
- Expansive 200K token context window at an entry-level cost structure
- Sub-250ms first-token latency for instant interactive experiences
- Dual availability as a hosted API and open weights
Considerations & Trade-offs
- Lower precision on multi-tiered logical deductions than the GLM-5 tier
- Can over-simplify answers to highly technical queries
MiniMax-M2.5 · Strengths & Key Advantages
- 48% SWE-bench Verified coding performance in an open-weights package
- Enhanced instruction following for strict programming paradigms
- Free from proprietary vendor lock-in or telemetry data tracking
Considerations & Trade-offs
- M2.7 surpasses it in both throughput and overall architectural reasoning
- High GPU memory requirement for unquantized cluster deployment
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7-Flash | MiniMax-M2.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.00 |
Output Tokens / Task Tokens generated per task | 12K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 6K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.7-Flash | MiniMax-M2.5 |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7-flash | minimax-m2-5 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-01 | 2026-02 |
| Knowledge Cutoff | November 2025 | December 2025 |
| Architecture | Optimized High-Speed Lightweight Transformer | Coding-Centric Sparse MoE Transformer |
| Parameters | Confidential | Confidential / 160B 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) | Flat rate ($0.015/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.002/1M tokens | 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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 99 GB VRAM (4-bit Quantized) · 344 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-4.7-Flash vs MiniMax-M2.5: Quick Answers
Which is more intelligent, GLM-4.7-Flash or MiniMax-M2.5?
MiniMax-M2.5 is more intelligent. It scores 82 compared with 74 on the composite Intelligence Index.
Which model is faster?
GLM-4.7-Flash is faster, generating 160 tokens per second compared with 80 tokens per second.
Which model has lower latency?
GLM-4.7-Flash has lower latency, with a time to first token of 0.24s compared with 0.47s.
Which model has a larger context window?
MiniMax-M2.5 has a larger context window, supporting 204K tokens compared with 200K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose GLM-4.7-Flash if your priority is High-concurrency chat routing, real-time customer engagement, and fast content generation. Its performance in demanding tasks justifies its operational token cost. Choose MiniMax-M2.5 if your workflow prioritizes On-premise code assistants, internal corporate automation, and technical data transformation. 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-4.7-Flash compare to MiniMax-M2.5 in API token pricing and cost per token?+
GLM-4.7-Flash costs $0.01 per 1M input tokens and $0.03 per 1M output tokens, whereas MiniMax-M2.5 costs $0.00 input and $0.00 output. Overall, MiniMax-M2.5 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: GLM-4.7-Flash or MiniMax-M2.5?+
GLM-4.7-Flash supports a context window of 200K tokens (~150k words), while MiniMax-M2.5 supports 204K tokens (~153k words). Therefore, MiniMax-M2.5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-4.7-Flash or MiniMax-M2.5?+
GLM-4.7-Flash scores at the frontier of coding benchmarks, while MiniMax-M2.5 scores 48%. For complex autonomous debugging and agentic engineering, MiniMax-M2.5 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GLM-4.7-Flash and MiniMax-M2.5?+
GLM-4.7-Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, MiniMax-M2.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-4.7-Flash and MiniMax-M2.5?+
GLM-4.7-Flash generates approximately 160 tokens per second, while MiniMax-M2.5 delivers 80 tokens per second. In latency-sensitive production environments, GLM-4.7-Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-4.7-Flash and MiniMax-M2.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to MiniMax-M2.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GLM-4.7-Flash. 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.
Related reading
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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