Openrouter/openai/GPT-5.6 Sol Pro

GPT-5.6 Sol Pro

Openrouter · LLM · #18 Rank Active
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85.2
Score
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Benchmark Scores

Overall Overall score composition: Reasoning 39% — 94.7; Code 20% — 85.4; Image 12% — 75.4; Text 24% — 77.0; Math 5% — 95.4
85.2
#24
BenchLM → Airating calculation
Code Airating rank — aggregate for Code (BenchLM / Airating calculation)
85.4
#24
BenchLM
BenchLM · Code — 85.4 (confidence 81%)
Text Airating rank — aggregate for Text (BenchLM / Airating calculation)
77.0
#117
BenchLM
BenchLM · Text — 77.0 (confidence 81%)
Image Airating rank — aggregate for Image (BenchLM / Airating calculation)
75.4
#88
BenchLM
BenchLM · Image — 75.4 (confidence 81%)
Math Airating rank — aggregate for Math (BenchLM / Airating calculation)
95.4
#13
BenchLM
BenchLM · Math — 95.4 (confidence 81%)
Reasoning Airating rank — aggregate for Reasoning (BenchLM / Airating calculation)
94.7
#12
BenchLM
BenchLM · Reasoning — 94.7 (confidence 81%)
Speed Output throughput (tokens/sec) from modelgrep live probes, or catalog estimate as fallback.
3.9
#378
modelgrep · 6 hours ago
Price Normalized API cost (input + output per 1M tokens) from OpenRouter and models.dev.
82.5
#524
models.dev · 6 hours ago

What the rating is based on

Overall score is a weighted composite. Primary data source: BenchLM.

85.2 BenchLM
  • Code 20% 85.4 BenchLM
    • BenchLM · Code 85.4 confidence 81%
  • Text 24% 77.0 BenchLM
    • BenchLM · Text 77.0 confidence 81%
  • Image 12% 75.4 BenchLM
    • BenchLM · Image 75.4 confidence 81%
  • Math 5% 95.4 BenchLM
    • BenchLM · Math 95.4 confidence 81%
  • Reasoning 39% 94.7 BenchLM
    • BenchLM · Reasoning 94.7 confidence 81%

How we calculate scores

Description

The GPT-5.6 Sol Pro by OpenAI is tailored specifically for complex reasoning and planning tasks, setting a new benchmark in deep work capabilities. Its robust multimodal functionality allows it to seamlessly integrate both text and visual inputs. With an extensive context window of 1,050,000 tokens, it can tackle intricate queries and process expansive datasets, making it ideal for analytical jobs in mathematics, programming, and advanced reasoning. The model is designed for professionals in research, data analysis, or software development who need precise outputs for high-stakes projects. However, users should be mindful of the pricing structure: $5 for input and $30 for output per million tokens. This may lead to significant costs for large-scale projects, which could be a deterrent for casual users. Additionally, while its reasoning capabilities shine, it may not be the best choice for quick, conversational interactions or simpler tasks that don't require deep analytical thought.

Strengths

  • Exceptional for complex reasoning tasks and problem-solving.
  • Supports both text and visual inputs for comprehensive analysis.
  • Highly effective for coding and algorithmic challenges.
  • Large context window enables handling of extensive datasets.

Limitations

  • Can become costly with extensive usage due to token pricing.
  • Less suited for casual or light conversational applications.
  • May exhibit latency in response times under heavy processing loads.

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