| Signal | gpt-oss-120b (free) | Delta | Jamba Large 1.7 |
|---|---|---|---|
Capabilities | 50 | -- | |
Pricing | 30 | +22 | |
Context window size | 81 | -5 | |
Recency | 89 | 0 | |
Output Capacity | 85 | +25 | |
| Overall Result | 2 wins | of 5 | 2 wins |
10
days higher
2
days
18
days higher
OpenAI
AI21 Labs
gpt-oss-120b (free) saves you $600.00/month
That's $7200.00/year compared to Jamba Large 1.7 at your current usage level of 100K calls/month.
| Metric | gpt-oss-120b (free) | Jamba Large 1.7 | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #211 | #210 | Jamba Large 1.7 |
| Quality Rank | #211 | #210 | Jamba Large 1.7 |
| Adoption Rank | #211 | #210 | Jamba Large 1.7 |
| Parameters | 120B | -- | -- |
| Context Window | 131K | 256K | Jamba Large 1.7 |
| Pricing | Free | $2.00/$8.00/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | gpt-oss-120b (free) |
| Pricing | 30 | 8 | gpt-oss-120b (free) |
| Context window size | 81 | 86 | Jamba Large 1.7 |
| Recency | 89 | 90 | Jamba Large 1.7 |
| Output Capacity | 85 | 60 | gpt-oss-120b (free) |
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%). Here's what the scores mean for these two models:
Scores 40/100 (rank #211), placing it in the top 28% of all 290 models tracked.
Scores 40/100 (rank #210), placing it in the top 28% of all 290 models tracked.
With only a 0-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.
Compare the cost per quality point to find the best value for your specific workload.
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 (free) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (256K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
gpt-oss-120b (free) and Jamba Large 1.7 are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
gpt-oss-120b (free)
Marginally better benchmark scores; both are excellent
Best for Cost
gpt-oss-120b (free)
100% lower pricing; better value at scale
Best for Reliability
gpt-oss-120b (free)
Higher uptime and faster response speeds
Best for Prototyping
gpt-oss-120b (free)
Stronger community support and better developer experience
Best for Production
gpt-oss-120b (free)
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | gpt-oss-120b (free) | Jamba Large 1.7 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
OpenAI
AI21 Labs
gpt-oss-120b (free) saves you $13.20/month
That's 100% cheaper than Jamba Large 1.7 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 (free) | Jamba Large 1.7 |
|---|---|---|
| Context Window | 131K | 256K |
| Max Output Tokens | 131,072 | 4,096 |
| Open Source | Yes | Yes |
| Created | Aug 5, 2025 | Aug 8, 2025 |
Both gpt-oss-120b (free) and Jamba Large 1.7 score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
gpt-oss-120b (free) is ranked #211 and Jamba Large 1.7 is ranked #210 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 (free) is cheaper at $0.00/M output tokens vs Jamba Large 1.7's $8.00/M output tokens - 8000.0x more expensive. Input token pricing: gpt-oss-120b (free) at $0.00/M vs Jamba Large 1.7 at $2.00/M.
Jamba Large 1.7 has a larger context window of 256,000 tokens compared to gpt-oss-120b (free)'s 131,072 tokens. A larger context window means the model can process longer documents and conversations.