| Signal | gpt-oss-120b | Delta | Llama 3.1 Nemotron 70B Instruct |
|---|---|---|---|
Capabilities | 67 | +17 | |
Benchmarks | 46 | -1 | |
Pricing | 100 | +1 | |
Context window size | 81 | -- | |
Recency | 88 | +54 | |
Output Capacity | 20 | -50 | |
| Overall Result | 3 wins | of 6 | 2 wins |
Score History
40.5
current score
Llama 3.1 Nemotron 70B Instruct
right now
44.6
current score
OpenAI
NVIDIA
gpt-oss-120b saves you $166.60/month
That's $1999.20/year compared to Llama 3.1 Nemotron 70B Instruct at your current usage level of 100K calls/month.
| Metric | gpt-oss-120b | Llama 3.1 Nemotron 70B Instruct | Winner |
|---|---|---|---|
| Overall Score | 41 | 45 | Llama 3.1 Nemotron 70B Instruct |
| Rank | #169 | #164 | Llama 3.1 Nemotron 70B Instruct |
| Quality Rank | #169 | #164 | Llama 3.1 Nemotron 70B Instruct |
| Adoption Rank | #169 | #164 | Llama 3.1 Nemotron 70B Instruct |
| Parameters | 120B | 70B | -- |
| Context Window | 131K | 131K | -- |
| Pricing | $0.04/$0.19/M | $1.20/$1.20/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | gpt-oss-120b |
| Benchmarks | 46 | 47 | Llama 3.1 Nemotron 70B Instruct |
| Pricing | 100 | 99 | gpt-oss-120b |
| Context window size | 81 | 81 | gpt-oss-120b |
| Recency | 88 | 35 | gpt-oss-120b |
| Output Capacity | 20 | 70 | Llama 3.1 Nemotron 70B Instruct |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 41/100 (rank #169), placing it in the top 42% of all 290 models tracked.
Scores 45/100 (rank #164), placing it in the top 44% of all 290 models tracked.
With only a 4-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
gpt-oss-120b offers 90% better value per quality point. At 1M tokens/day, you'd spend $3.44/month with gpt-oss-120b vs $36.00/month with Llama 3.1 Nemotron 70B Instruct - a $32.56 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. gpt-oss-120b also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.19/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (45/100) correlates with better nuance, coherence, and style in long-form content
Llama 3.1 Nemotron 70B Instruct has a moderate advantage with a 4.100000000000001-point lead in composite score. It wins on more signal dimensions, but gpt-oss-120b has specific strengths that could make it the better choice for certain workflows.
Best for Quality
gpt-oss-120b
Marginally better benchmark scores; both are excellent
Best for Cost
gpt-oss-120b
90% lower pricing; better value at scale
Best for Reliability
gpt-oss-120b
Higher uptime and faster response speeds
Best for Prototyping
gpt-oss-120b
Stronger community support and better developer experience
Best for Production
gpt-oss-120b
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | gpt-oss-120b | Llama 3.1 Nemotron 70B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
OpenAI
NVIDIA
gpt-oss-120b saves you $3.30/month
That's 92% cheaper than Llama 3.1 Nemotron 70B Instruct at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | gpt-oss-120b | Llama 3.1 Nemotron 70B Instruct |
|---|---|---|
| Context Window | 131K | 131K |
| Max Output Tokens | -- | 16,384 |
| Open Source | Yes | Yes |
| Created | Aug 5, 2025 | Oct 15, 2024 |
Llama 3.1 Nemotron 70B Instruct scores 45/100 (rank #164) compared to gpt-oss-120b's 41/100 (rank #169), giving it a 4-point advantage. Llama 3.1 Nemotron 70B Instruct is the stronger overall choice, though gpt-oss-120b may excel in specific areas like cost efficiency.
gpt-oss-120b is ranked #169 and Llama 3.1 Nemotron 70B Instruct is ranked #164 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
gpt-oss-120b is cheaper at $0.19/M output tokens vs Llama 3.1 Nemotron 70B Instruct's $1.20/M output tokens - 6.3x more expensive. Input token pricing: gpt-oss-120b at $0.04/M vs Llama 3.1 Nemotron 70B Instruct at $1.20/M.
gpt-oss-120b has a larger context window of 131,072 tokens compared to Llama 3.1 Nemotron 70B Instruct's 131,072 tokens. A larger context window means the model can process longer documents and conversations.