GLM-5-Turbo vs MiniMax-M2.7-highspeed
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GLM-5-Turbo by Zhipu AI and MiniMax-M2.7-highspeed 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-M2.7-highspeed holds a notable pricing advantage for token ingestion. On output generation, MiniMax-M2.7-highspeed is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, MiniMax-M2.7-highspeed provides an expanded memory horizon with 204K tokens. In latency-sensitive production environments, MiniMax-M2.7-highspeed 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-5-Turbo
GLM-5 más rápidoHosted comercial accelerator del GLM-5 arquitectura que ofrece reduced latencia y optimized herramienta ejecución para demanding empresarial apps.
$0.60 / 1M
$1.80 / 1M
200K tokens
55%
MiniMax-M2.7-highspeed
Buque insignia rápidorápido cloud API variant de M2.7 optimizado para en tiempo real agente bucles, interactivo code análisis, y alto-volumen corporativo canalizaciones.
$0.25 / 1M
$1.00 / 1M
204K tokens
N/A
Diferencias Clave de un Vistazo
- MiniMax-M2.7-highspeed is 2.0x cheaper for API usage (input + output tokens combined).
- MiniMax-M2.7-highspeed has a larger context window (204K vs 200K tokens).
- MiniMax-M2.7-highspeed is faster at 140 tok/s vs 95 tok/s.
| Característica | GLM-5-Turbo | MiniMax-M2.7-highspeed |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 82 |
Provider | Zhipu AI | MiniMax |
Input Price Cost per million input tokens | $0.60 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $1.80 / 1M | $1.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.34 / 1M | $0.17 / 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 | 16K | 8K |
Throughput Speed Tokens generated per second | 95 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 55% | N/A |
GPQA Diamond Graduate-level reasoning | 65% | 63% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.03 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-5-Turbo · Strengths & Key Advantages
- Reduced latencia y swift tiempo-un-primer-token en comparación con raw de código abierto clúster servicio
- confiable empresarial SLA backed por official Zhipu Cloud infraestructura
- económico $0.60/1M entrada precios para insignia-grade inteligencia
Considerations & Trade-offs
- propietario hosted endpoint rather que freely downloadable weights
- ventana de contexto es 200K en comparación con 1M en el upcoming GLM-5.2 nivel
MiniMax-M2.7-highspeed · Strengths & Key Advantages
- 140 tokens/sec transmisión velocity con solid 1335 Arena Elo rendimiento
- excelente balance entre insignia razonamiento y snappy interactivo latencia
- económico hosted precios ($0.25/1M entrada) con 204K ventana de contexto
Considerations & Trade-offs
- Slightly menor theoretical profundidad que el completo unaccelerated M2.7 modelo
- propietario hosted endpoint rather que de pesos abiertos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-5-Turbo | MiniMax-M2.7-highspeed |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.16 | $0.06 |
Output Tokens / Task Tokens generated per task | 22K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 2K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GLM-5-Turbo | MiniMax-M2.7-highspeed |
|---|---|---|
| Official API Model ID (SDK) | glm-5-turbo | minimax-m2-7-highspeed |
| API Protocol | Zhipu AI REST API (OpenAI-Compatible) | MiniMax REST API (OpenAI-Compatible) |
| Release Date | 2026-03 | 2026-03 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Hosted Accelerated Frontier Transformer | High-Throughput Hosted Cloud Accelerator |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.6/1M in) across full 200K window | Flat rate ($0.25/1M in) across full 204K window |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Cached input at $0.025/1M tokens |
| 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 | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| 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 | Zhipu AI Production Safety & Alignment Verified | MiniMax Production Safety & Alignment 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 | text |
| Output Modalities | text | text |
GLM-5-Turbo vs MiniMax-M2.7-highspeed: Respuestas Rápidas (FAQ)
Which is more intelligent, GLM-5-Turbo or MiniMax-M2.7-highspeed?
GLM-5-Turbo is more intelligent. It scores 86 compared with 82 on the composite Intelligence Index.
Which model is faster?
MiniMax-M2.7-highspeed is faster, generating 140 tokens per second compared with 95 tokens per second.
Which model is cheaper to run?
MiniMax-M2.7-highspeed is cheaper at $0.17 per 1M tokens blended, compared with $0.34 (7:2:1 cache:input:output ratio).
Which model has lower latency?
MiniMax-M2.7-highspeed has lower latency, with a time to first token of 0.29s compared with 0.42s.
Which model has a larger context window?
MiniMax-M2.7-highspeed has a larger context window, supporting 204K tokens compared with 200K tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose GLM-5-Turbo if your priority is High-throughput enterprise applications, code assistants, and automated customer workflows. Its performance in demanding tasks justifies its operational token cost. Choose MiniMax-M2.7-highspeed if your workflow prioritizes High-frequency developer copilots, automated ticket triage, and responsive agents. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does GLM-5-Turbo compare to MiniMax-M2.7-highspeed in API token pricing and cost per token?+
GLM-5-Turbo costs $0.60 per 1M input tokens and $1.80 per 1M output tokens, whereas MiniMax-M2.7-highspeed costs $0.25 input and $1.00 output. Overall, MiniMax-M2.7-highspeed 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-5-Turbo or MiniMax-M2.7-highspeed?+
GLM-5-Turbo supports a context window of 200K tokens (~150k words), while MiniMax-M2.7-highspeed supports 204K tokens (~153k words). Therefore, MiniMax-M2.7-highspeed can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-5-Turbo or MiniMax-M2.7-highspeed?+
GLM-5-Turbo scores 55% on SWE-bench Verified, while MiniMax-M2.7-highspeed scores competitively for software generation. For complex autonomous debugging and agentic engineering, GLM-5-Turbo provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GLM-5-Turbo and MiniMax-M2.7-highspeed?+
GLM-5-Turbo features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, MiniMax-M2.7-highspeed provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-5-Turbo and MiniMax-M2.7-highspeed?+
GLM-5-Turbo generates approximately 95 tokens per second, while MiniMax-M2.7-highspeed delivers 140 tokens per second. In latency-sensitive production environments, MiniMax-M2.7-highspeed provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-5-Turbo and MiniMax-M2.7-highspeed to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to MiniMax-M2.7-highspeed to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GLM-5-Turbo. 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs