GLM-4.7-FlashX vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.7-FlashX by Zhipu AI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
GLM-4.7-FlashX
高度な推論モデルZhipu AI が提供する GLM-4.7-FlashX は、最大 200K トークンのコンテキストウィンドウを備え、入力 $0.02/100万トークン・出力 $0.04/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.02 / 1M
$0.04 / 1M
200K tokens
N/A
GPT-6.1 Sol
高度な推論モデルOpenAI が提供する GPT-6.1 Sol は、最大 1M トークンのコンテキストウィンドウを備え、入力 $2.0/100万トークン・出力 $10.0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
ひと目でわかる主な違い
- GLM-4.7-FlashX is 172.3x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 200K tokens).
- GLM-4.7-FlashX is faster at 150 tok/s vs 120 tok/s.
| Feature | GLM-4.7-FlashX | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 94 |
Provider | Zhipu AI | OpenAI |
Input Price Cost per million input tokens | $0.02 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.04 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $1.47 / 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 | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.7-FlashX · Strengths & Key Advantages
- Zhipu AI の高度な Enhanced Latency-Optimized Transformer アーキテクチャと 200K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.02/1M、出力 $0.04/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
GPT-6.1 Sol · Strengths & Key Advantages
- OpenAI の高度な Near-Astra unified reasoning model アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2.0/1M、出力 $10.0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7-FlashX | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $2.05 |
Output Tokens / Task Tokens generated per task | 13K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 36K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.7-FlashX | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7-flashx | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-01 | 2026-09 |
| Knowledge Cutoff | November 2025 | April 2026 |
| Architecture | Enhanced Latency-Optimized Transformer | Near-Astra unified reasoning model |
| 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 | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.02/1M in) across full 200K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.003/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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 GPT-6.1 Sol: よくある質問 (FAQ)
Which is more intelligent, GLM-4.7-FlashX or GPT-6.1 Sol?
GPT-6.1 Sol 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 120 tokens per second.
Which model is cheaper to run?
GLM-4.7-FlashX is cheaper at $0.01 per 1M tokens blended, compared with $1.47 (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 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 200K tokens.
総合評価
Choose GLM-4.7-FlashX for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、zhipu ai エコシステムの統合, or choose GPT-6.1 Sol for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、openai エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: GLM-4.7-FlashX or GPT-6.1 Sol?+
GLM-4.7-FlashX costs $0.02/1M input and $0.04/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: GLM-4.7-FlashX or GPT-6.1 Sol?+
GLM-4.7-FlashX supports 200K tokens whereas GPT-6.1 Sol 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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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.
公的ソース: Zhipu AI & OpenAI Official Documentation