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gpt-oss-120b (batch) vs Mercury 2.5

vs
Mercury 2.5

Inception

61#208
Signal-by-Signal Comparison
Signalgpt-oss-120b (batch)DeltaMercury 2.5
Capabilities
67
--
67
Benchmarks
60
+1
60
Pricing
99
0
100
Context window size
81
-5
86
Recency
60
-40
100
Output Capacity
81
+4
77
Overall Result
2 wins
of 6
3 wins
Mercury 2.5 wins 3 of 6 signals

Score History

Score History (3 data points)
gpt-oss-120b (batch)Mercury 2.5
gpt-oss-120b (batch)

61.6

current score

Leader

gpt-oss-120b (batch)

right now

Mercury 2.5

61.3

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

gpt-oss-120b (batch)

OpenAI

Per request$0.000450
Daily$1.50
Monthly$45.00
Annual$540.00

Mercury 2.5

Inception

Best Value
Per request$0.000115
Daily$0.38
Monthly$11.50
Annual$138.00

Mercury 2.5 saves you $33.50/month

That's $402.00/year compared to gpt-oss-120b (batch) at your current usage level of 100K calls/month.

74% cheaper
Choose Mercury 2.5 for cost optimization

gpt-oss-120b (batch) pricing:
Input:$0.15/M tokens
Output:$0.60/M tokens
Mercury 2.5 pricing:
Input:$0.04/M tokens
Output:$0.15/M tokens
Winner
gpt-oss-120b (batch)

OpenAI

62

Composite Score

Mercury 2.5

Inception

61

Composite Score

Signal-by-Signal Comparison
Metricgpt-oss-120b (batch)Mercury 2.5Winner
Overall Score
62
61
gpt-oss-120b (batch)
Rank#206#208
gpt-oss-120b (batch)
Quality Rank#206#208
gpt-oss-120b (batch)
Adoption Rank#206#208
gpt-oss-120b (batch)
Parameters120B----
Context Window131K260K
Mercury 2.5
Pricing$0.15/$0.60/M$0.04/$0.15/M--
Signal Scores
Capabilities
67
67
gpt-oss-120b (batch)
Benchmarks
60
60
gpt-oss-120b (batch)
Pricing
99
100
Mercury 2.5
Context window size
81
86
Mercury 2.5
Recency
60
100
Mercury 2.5
Output Capacity
81
77
gpt-oss-120b (batch)
Benchmark Head-to-Head(1 benchmarks)
gpt-oss-120b (batch): 1Mercury 2.5: 0
gpt-oss-120b (batch)
Mercury 2.5
Normalized 0-100%
Arena Elo
13521347
Benchmark Interpretation

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.

gpt-oss-120b (batch)Competitive

Scores 62/100 (rank #206), placing it in the top 29% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Mercury 2.5Competitive

Scores 61/100 (rank #208), placing it in the top 29% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

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.

When to Use Each Model

Choose gpt-oss-120b (batch) when you need:

  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model

Choose Mercury 2.5 when you need:

  • High-volume production workloads where API costs must be minimized
  • Processing long documents or large codebases (260K token context)
  • Step-by-step reasoning and chain-of-thought problem solving
Cost-Performance Analysis
gpt-oss-120b (batch)
Input cost$0.15/M tokens
Output cost$0.60/M tokens
Cost per quality point$0.012
Est. monthly (1M tokens/day)$11.25
Mercury 2.5Best Value
Input cost$0.04/M tokens
Output cost$0.15/M tokens
Cost per quality point$0.003
Est. monthly (1M tokens/day)$2.85

Mercury 2.5 offers 75% better value per quality point. At 1M tokens/day, you'd spend $2.85/month with Mercury 2.5 vs $11.25/month with gpt-oss-120b (batch) - a $8.40 monthly difference.

Latency & Speed
gpt-oss-120b (batch)Faster
Speed score0/100
Mercury 2.5
Speed score0/100

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.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

gpt-oss-120b (batch)

Customer support chatbot

Suitable for user-facing chat with competitive response times. Mercury 2.5 also offers lower per-token costs for high-volume support

gpt-oss-120b (batch)

Long document analysis

Larger context window (260K tokens) can process longer documents, contracts, and research papers in a single pass

Mercury 2.5

Batch data extraction

Lower output pricing ($0.15/M) reduces costs when processing thousands of records daily

Mercury 2.5

Creative writing & content

Higher overall composite score (62/100) correlates with better nuance, coherence, and style in long-form content

gpt-oss-120b (batch)
Which Should You Choose?
Our recommendation:
gpt-oss-120b (batch)

gpt-oss-120b (batch) and Mercury 2.5 are extremely close in overall performance (only 0.30000000000000426 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

by OpenAI

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale

by Inception

  • Choose for Cost - 75% lower pricing; better value at scale
Capability Comparison
Capabilitygpt-oss-120b (batch)Mercury 2.5
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoning
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

gpt-oss-120b (batch)

OpenAI

$0.9900
estimated monthly cost

Mercury 2.5

Inception

Best Value
$0.2520
estimated monthly cost

Mercury 2.5 saves you $0.7380/month

That's 75% cheaper than gpt-oss-120b (batch) 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.

Parameters & Context
Parametergpt-oss-120b (batch)Mercury 2.5
Context Window131K260K
Max Output Tokens117,96465,536
Open SourceYesNo
CreatedAug 5, 2025Sep 8, 2026
Last updated: 26m ago

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