GLM-4.7-FlashX vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.7-FlashX by Zhipu AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
GLM-4.7-FlashX
Modèle Léger et ÉconomiqueGLM-4.7-FlashX de Zhipu AI est un modèle d’IA conçu pour une efficacité économique maximale par token et une exécution légère à grande échelle, offrant une fenêtre de contexte de 200K, une vitesse de 150 tokens/s, un indice d’intelligence d
$0.02 / 1M
$0.04 / 1M
200K tokens
N/A
Claude Opus 5.5
Modèle Phare de PointeClaude Opus 5.5 de Anthropic est un modèle d’IA conçu pour le raisonnement d’entreprise de pointe, l’ingénierie logicielle et la production IA, offrant une fenêtre de contexte de 1M, une vitesse de 78 tokens/s, un indice d’intelligence de 9
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Différences Clés en un Coup d’Œil
- GLM-4.7-FlashX is 348.9x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 200K tokens).
- GLM-4.7-FlashX is faster at 150 tok/s vs 78 tok/s.
| Caractéristique | GLM-4.7-FlashX | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 94 |
Provider | Zhipu AI | Anthropic |
Input Price Cost per million input tokens | $0.02 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.04 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 150 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 84.6% |
GPQA Diamond Graduate-level reasoning | N/A | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.7-FlashX · Strengths & Key Advantages
- Fenêtre de contexte de 200K et capacité de sortie maximale de 8K tokens (MMLU-Pro 74.5% · Arena Elo 1290)
- Débit rapide de 150 tokens/seconde avec une latence au premier token (TTFT) de 260 ms
- Poids ouverts permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Claude Opus 5.5 · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 128K tokens (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
- Débit rapide de 78 tokens/seconde avec une latence au premier token (TTFT) de 1400 ms
- Tarification API optimisée ($4/$20 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7-FlashX | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $3.85 |
Output Tokens / Task Tokens generated per task | 13K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 42K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GLM-4.7-FlashX | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7-flashx | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-01 | 2026-09 |
| Knowledge Cutoff | November 2025 | June 2026 |
| Architecture | Enhanced Latency-Optimized Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.02/1M in) across full 200K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.003/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
GLM-4.7-FlashX vs Claude Opus 5.5: Réponses Rapides (FAQ)
Which is more intelligent, GLM-4.7-FlashX or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 76 on the composite Intelligence Index.
Which model is faster?
GLM-4.7-FlashX is faster, generating 150 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
GLM-4.7-FlashX is cheaper at $0.01 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-4.7-FlashX has lower latency, with a time to first token of 0.26s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 200K tokens.
Verdict
Choose GLM-4.7-FlashX for conçu pour une efficacité économique maximale par token et une exécution légère à grande échelle, l’intégration rag et les pipelines de production évolutifs., or choose Claude Opus 5.5 for conçu pour le raisonnement d’entreprise de pointe, l’ingénierie logicielle et la production ia, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: GLM-4.7-FlashX or Claude Opus 5.5?+
GLM-4.7-FlashX costs $0.02/1M input and $0.04/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: GLM-4.7-FlashX or Claude Opus 5.5?+
GLM-4.7-FlashX supports 200K tokens whereas Claude Opus 5.5 supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Zhipu AI & Anthropic Official Documentation