Llama 3.2 90B Vision vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.2 90B Vision by Meta against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Llama 3.2 90B Vision
Open Vision FlagshipMeta’s premier open multimodal foundation model pairing 128K context windows with cross-attention visual encoders to interpret complex charts, graphs, and images.
$0.00 / 1M
$0.00 / 1M
128K tokens
28.5%
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%
Key Differences at a Glance
- Llama 3.2 90B Vision is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 75 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 28.5%).
| Feature | Llama 3.2 90B Vision | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 94 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 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 | 75 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 28.5% | 82.3% |
GPQA Diamond Graduate-level reasoning | 48.2% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 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.
Llama 3.2 90B Vision · Strengths & Key Advantages
- Native open visual reasoning across complex documents, schematics, and maps
- Cross-attention adapter architecture preserves text reasoning while unlocking vision
- 128,000 token context window
Considerations & Trade-offs
- Requires specialized multi-GPU servers for high-throughput visual batching
- Output generation capped at 8,192 tokens
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.2 90B Vision | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.05 |
Output Tokens / Task Tokens generated per task | 24K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.2 90B Vision | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-90B-Vision | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | December 2023 | April 2026 |
| Architecture | Multimodal Adapter-Fused GQA Transformer | Near-Astra unified reasoning model |
| Parameters | 90B | 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | 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) · 56 GB VRAM (4-bit Quantized) · 194 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 |
Llama 3.2 90B Vision vs GPT-6.1 Sol: Quick Answers
Which is more intelligent, Llama 3.2 90B Vision or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 44 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?
Llama 3.2 90B Vision is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.2 90B Vision has lower latency, with a time to first token of 0.58s 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 128K tokens.
Verdict
Choose Llama 3.2 90B Vision for self-hosted ocr extraction, visual document analysis, and private enterprise multimodal agents, or choose GPT-6.1 Sol for the best value frontier model for professional and agentic work.
Frequently asked questions
Which is cheaper: Llama 3.2 90B Vision or GPT-6.1 Sol?+
Llama 3.2 90B Vision costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama 3.2 90B Vision or GPT-6.1 Sol?+
Llama 3.2 90B Vision supports 128K 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.
Sources: Meta & OpenAI Official Documentation