gpt-oss-120b Open vs INMercury 2.5 Paid
gpt-oss-120b vs Mercury 2.5 compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$0.17 /M outputNo per-token fees if you self-host
$0.15 /M outputManaged API (no infra to run)
gpt-oss-120b vs Mercury 2.5 specs
| Spec | gpt-oss-120b | Mercury 2.5 | Winner |
|---|---|---|---|
| Maker | OpenAI | Inception | – |
| Type | Open-weight | Proprietary | |
| Context window | 131K tokens | 260K tokens | INMercury 2.5 |
| Input price | $0.04/M · free self-host | $0.04/M | = Tie |
| Output price | $0.17/M · free self-host | $0.15/M | INMercury 2.5 |
| Vision / multimodal | No | No | – |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | |
| License | Apache 2.0 | Proprietary |
Price gap & when to choose each
- You want to self-host or run on your cloud
- You prioritize data privacy & control
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You need the longer 260K context window
- You want the lower output price
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✗ | ✗ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✗ | ✗ |
Benchmarks: gpt-oss-120b vs Mercury 2.5
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How gpt-oss-120b and Mercury 2.5 score
| Criterion | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| Cost-efficiency | 5.0 | 5.0 |
| Context window | 3.5 | 4.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 3.5 | 3.5 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
What each model is
gpt-oss-120b Open
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimize
Mercury 2.5 Paid
Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple tokens in parallel, achieving...
Other models in these families
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Frequently asked questions
Is gpt-oss-120b as good as Mercury 2.5?
gpt-oss-120b is open-weight and competitive on many tasks, but Mercury 2.5 may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Can I run gpt-oss-120b locally?
Yes. gpt-oss-120b has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Mercury 2.5 is API-only and cannot be self-hosted.
gpt-oss-120b vs Mercury 2.5 — which should I pick in 2026?
Choose gpt-oss-120b if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Mercury 2.5 if you want frontier capability through a managed API with zero infrastructure to run.
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