GPT-6.1 Sol vs Aya Expanse 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-6.1 Sol by OpenAI against Aya Expanse 8B by Cohere across real-world coding, reasoning, token economics, and latency.
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GPT-6.1 Sol
Near-Astra ValueDelivers results close to GPT-6 Astra at roughly one fifth of the price — the latest 2026 release.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Aya Expanse 8B
MultilingualCompact open multilingual model meticulously trained across 23 languages, bridging language divides in low-resource linguistic environments.
$0.08 / 1M
$0.16 / 1M
8.2K tokens
24%
Key Differences at a Glance
- Aya Expanse 8B is 41.3x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 8K tokens).
- Aya Expanse 8B is faster at 135 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 24.0%).
| Feature | GPT-6.1 Sol | Aya Expanse 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 36 |
Provider | OpenAI | Cohere |
Input Price Cost per million input tokens | $2.00 / 1M | $0.08 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.16 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 128K | 4K |
Throughput Speed Tokens generated per second | 120 tok/s | 135 tok/s |
SWE-bench Verified Real-world software engineering | 82.3% | 24% |
GPQA Diamond Graduate-level reasoning | 80.8% | 40% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.02 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results at roughly a fifth of the cost
- Excellent coding and long-horizon agent performance
- Strong reliability with far lower latency than Astra
- Near-flagship results at roughly a fifth of the price
Considerations & Trade-offs
- Still marginally behind Astra on the very hardest reasoning
- Same price as GPT-6 Sol, so the upgrade decision is easy
Aya Expanse 8B · Strengths & Key Advantages
- Purpose-built dataset covering 23 diverse languages with superior cultural context
- Lightweight 8B footprint running smoothly on consumer and laptop GPUs
- Strong multilingual translation and cross-lingual text summarization
Considerations & Trade-offs
- Context window limited to 8K tokens
- Lower capability on advanced coding and competition mathematics
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6.1 Sol | Aya Expanse 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.05 | $0.04 |
Output Tokens / Task Tokens generated per task | 62K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6.1 Sol | Aya Expanse 8B |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-1-sol | aya-expanse-8b |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2024-10 |
| Knowledge Cutoff | April 2026 | August 2024 |
| Architecture | Near-Astra unified reasoning model | Compact Multilingual Dense Transformer |
| Parameters | Confidential | 8B |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 1000K window | Flat rate ($0.08/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.1/1M (no extra cache-write fee) | Cached input at $0.02/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
GPT-6.1 Sol vs Aya Expanse 8B: Quick Answers
Which is more intelligent, GPT-6.1 Sol or Aya Expanse 8B?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 36 on the composite Intelligence Index.
Which model is faster?
Aya Expanse 8B is faster, generating 135 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Aya Expanse 8B is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Expanse 8B has lower latency, with a time to first token of 0.30s 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 8.2K tokens.
Verdict
Choose GPT-6.1 Sol for the best value frontier model for professional and agentic work, or choose Aya Expanse 8B for multilingual customer interaction, low-resource language translation, and edge deployments.
Frequently asked questions
Which is cheaper: GPT-6.1 Sol or Aya Expanse 8B?+
GPT-6.1 Sol costs $2/1M input and $10/1M output tokens, while Aya Expanse 8B costs $0.08/1M input and $0.16/1M output tokens.
Which model has a larger context window: GPT-6.1 Sol or Aya Expanse 8B?+
GPT-6.1 Sol supports 1M tokens whereas Aya Expanse 8B supports 8K 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.
Sources: OpenAI & Cohere Official Documentation