GPT-OSS 20B vs Llama 3.1 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-OSS 20B by OpenAI and Llama 3.1 8B by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 3.1 8B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.1 8B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-OSS 20B provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, both models deliver balanced throughput and low time-to-first-token latency for interactive conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GPT-OSS 20B
Open ReasoningAn open-weight GPT reasoning model for self-hosted agents and controllable deployments.
$0.10 / 1M
$0.50 / 1M
131.1K tokens
32.5%
Llama 3.1 8B
Small Open LlamaThe premier compact open model of 2024 with 128K context, multilingual fluency, and native tool use.
$0.00 / 1M
$0.00 / 1M
128K tokens
22%
Key Differences at a Glance
- Llama 3.1 8B is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-OSS 20B has a larger context window (131K vs 128K tokens).
- Llama 3.1 8B is faster at 160 tok/s vs 160 tok/s.
- GPT-OSS 20B leads on SWE-bench coding (32.5% vs 22.0%).
| Feature | GPT-OSS 20B | Llama 3.1 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 60 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $0.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.50 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 33K | 8K |
Throughput Speed Tokens generated per second | 160 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 32.5% | 22% |
GPQA Diamond Graduate-level reasoning | 55% | 38% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 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-OSS 20B · Strengths & Key Advantages
- Open weights — run it fully on your own hardware
- Small enough for a laptop or single consumer GPU
- Cheap on hosted providers and supports tool calling
- Runs comfortably on consumer hardware
Considerations & Trade-offs
- Clearly weaker than frontier models on hard reasoning
- Text-only — no vision or audio input
- Needs careful prompt tuning to reach its best output
Llama 3.1 8B · Strengths & Key Advantages
- Exceptional intelligence for an 8B model
- Full 128K context window with clean recall
- Massive open-source developer ecosystem
Considerations & Trade-offs
- Smaller capacity limits complex math and architectural code reasoning
- Text-only model
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-OSS 20B | Llama 3.1 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.00 |
Output Tokens / Task Tokens generated per task | 40K | 4K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-OSS 20B | Llama 3.1 8B |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-20b | meta-llama/Llama-3.1-8B |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-08 | 2024-07 |
| Knowledge Cutoff | June 2024 | December 2023 |
| Architecture | Sparse MoE transformer with reasoning | Dense auto-regressive multilingual transformer |
| Parameters | 21B (3.6B active MoE) | 8B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 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 ($0.1/1M in) across full 131K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.01/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 13 GB VRAM (4-bit Quantized) · 45 GB VRAM (Native BF16) | 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) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
GPT-OSS 20B vs Llama 3.1 8B: Quick Answers
Which is more intelligent, GPT-OSS 20B or Llama 3.1 8B?
Llama 3.1 8B is more intelligent. It scores 60 compared with 46 on the composite Intelligence Index.
Which model is cheaper to run?
Llama 3.1 8B is cheaper at $0.00 per 1M tokens blended, compared with $0.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.1 8B has lower latency, with a time to first token of 0.22s compared with 0.26s.
Which model has a larger context window?
GPT-OSS 20B has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-OSS 20B if your priority is Self-hosted agents, on-premise deployments and privacy-sensitive work. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.1 8B if your workflow prioritizes Local offline development, edge device inference, and cost-free SaaS backends. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does GPT-OSS 20B compare to Llama 3.1 8B in API token pricing and cost per token?+
GPT-OSS 20B costs $0.10 per 1M input tokens and $0.50 per 1M output tokens, whereas Llama 3.1 8B costs $0.00 input and $0.00 output. Overall, Llama 3.1 8B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-OSS 20B or Llama 3.1 8B?+
GPT-OSS 20B supports a context window of 131K tokens (~98k words), while Llama 3.1 8B supports 128K tokens (~96k words). Therefore, GPT-OSS 20B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-OSS 20B or Llama 3.1 8B?+
GPT-OSS 20B scores 32.5% on SWE-bench Verified while Llama 3.1 8B scores 22%. For autonomous software engineering and multi-file debugging, GPT-OSS 20B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-OSS 20B and Llama 3.1 8B?+
GPT-OSS 20B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 3.1 8B focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-OSS 20B and Llama 3.1 8B?+
Both GPT-OSS 20B and Llama 3.1 8B deliver matching throughput at approximately 160 tokens per second. In latency-sensitive production environments, streaming response velocity is comparable, with real-world time-to-first-token (TTFT) determined primarily by provider routing and concurrency load.
Can you implement a tiered LLM routing setup combining GPT-OSS 20B and Llama 3.1 8B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.1 8B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-OSS 20B. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs