Yi-Large vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Yi-Large by 01.AI against GPT-6.1 Sol by OpenAI 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.
Yi-Large
Modèle de Génération ClassiqueYi-Large de 01.AI est un modèle d’IA conçu pour les assistants conversationnels, la synthèse documentaire et les usages professionnels, offrant une fenêtre de contexte de 33K, une vitesse de 75 tokens/s, un indice d’intelligence de 79/100 e
$2.50 / 1M
$2.50 / 1M
32.8K tokens
N/A
GPT-6.1 Sol
Modèle Phare de PointeGPT-6.1 Sol de OpenAI 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 120 tokens/s, un indice d’intelligence de 94/100
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Différences Clés en un Coup d’Œil
- Yi-Large is 1.9x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 33K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 75 tok/s.
| Caractéristique | Yi-Large | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 79 | 94 |
Provider | 01.AI | OpenAI |
Input Price Cost per million input tokens | $2.50 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.50 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.93 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | 52% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.10 / 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.
Yi-Large · Strengths & Key Advantages
- Fenêtre de contexte de 33K et capacité de sortie maximale de 4K tokens (GPQA Diamond 52% · MMLU-Pro 76.5%)
- Débit rapide de 75 tokens/seconde avec une latence au premier token (TTFT) de 510 ms
- Tarification API optimisée ($2.5/$2.5 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
GPT-6.1 Sol · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 128K tokens (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- Débit rapide de 120 tokens/seconde avec une latence au premier token (TTFT) de 1120 ms
- Tarification API optimisée ($2/$10 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 | Yi-Large | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $2.05 |
Output Tokens / Task Tokens generated per task | 14K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Yi-Large | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | yi-large | gpt-6-1-sol |
| API Protocol | 01.AI REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-05 | 2026-09 |
| Knowledge Cutoff | March 2024 | April 2026 |
| Architecture | Dense Bilingual Foundation Transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2.5/1M in) across full 33K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M 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 | API Prompt Engineering & RAG Distillation | 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 | 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 | 01.AI Production Safety & Alignment Verified | OpenAI Preparedness Framework Audited & System Card Published |
| 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, vision |
| Output Modalities | text | text |
Yi-Large vs GPT-6.1 Sol: Réponses Rapides (FAQ)
Which is more intelligent, Yi-Large or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 79 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Yi-Large is cheaper at $0.93 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Yi-Large has lower latency, with a time to first token of 0.51s 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 32.8K tokens.
Verdict
Choose Yi-Large for conçu pour les assistants conversationnels, la synthèse documentaire et les usages professionnels, l’intégration rag et les pipelines de production évolutifs., or choose GPT-6.1 Sol 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: Yi-Large or GPT-6.1 Sol?+
Yi-Large costs $2.5/1M input and $2.5/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Yi-Large or GPT-6.1 Sol?+
Yi-Large supports 33K 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: 01.AI & OpenAI Official Documentation