Aya Vision 8B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Aya Vision 8B by Cohere against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Aya Vision 8B
开源权重模型Cohere 推出的 Aya Vision 8B 人工智能模型，具备 16K 上下文窗口，输入价格为 $0.1/百万 Token，输出价格为 $0.25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.10 / 1M
$0.25 / 1M
16K tokens
22%
GPT-6.1 Sol
深度推理模型OpenAI 推出的 GPT-6.1 Sol 人工智能模型，具备 1M 上下文窗口，输入价格为 $2.0/百万 Token，输出价格为 $10.0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
核心差异一览
- Aya Vision 8B is 28.9x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 16K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 110 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 22.0%).
| Feature | Aya Vision 8B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 94 |
Provider | Cohere | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.25 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 16K tokens~24 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 | 110 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 22% | 82.3% |
GPQA Diamond Graduate-level reasoning | 41% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.10 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Aya Vision 8B · Strengths & Key Advantages
- 由 Cohere 研发的高精度 Multilingual Vision-Language Transformer 架构，支持 16K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.1/1M，输出 $0.25/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
GPT-6.1 Sol · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Near-Astra unified reasoning model 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $2.0/1M，输出 $10.0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Aya Vision 8B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $2.05 |
Output Tokens / Task Tokens generated per task | 18K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Aya Vision 8B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | aya-vision-8b | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-03 | 2026-09 |
| Knowledge Cutoff | January 2025 | April 2026 |
| Architecture | Multilingual Vision-Language Transformer | Near-Astra unified reasoning model |
| Parameters | 8B | 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 ($0.1/1M in) across full 16K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M 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) · Vision (~258–765 tokens per image tile) | 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) · 5 GB VRAM (4-bit Quantized) · 17 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, vision | text, vision |
| Output Modalities | text | text |
Aya Vision 8B vs GPT-6.1 Sol: 常见问题解答 (FAQ)
Which is more intelligent, Aya Vision 8B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 38 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
Aya Vision 8B is cheaper at $0.07 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Vision 8B has lower latency, with a time to first token of 0.35s 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 16K tokens.
综合结论
Choose Aya Vision 8B for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 cohere 生态深度集成, or choose GPT-6.1 Sol for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 openai 生态深度集成.
常见问题 (FAQ)
Which is cheaper: Aya Vision 8B or GPT-6.1 Sol?+
Aya Vision 8B costs $0.1/1M input and $0.25/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Aya Vision 8B or GPT-6.1 Sol?+
Aya Vision 8B supports 16K 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.
数据来源: Cohere & OpenAI Official Documentation