Agents-A1
BenchLM is tracking Agents-A1, but this profile is currently excluded from the public leaderboard because it still lacks enough non-generated benchmark coverage to rank safely. Only non-generated public benchmark rows appear below.
Agents-A1 is a open weight model with a 262K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
Agents-A1 sits inside the Agents-A1 family alongside Agents-A1-F16-GGUF, Agents-A1-FP8, Agents-A1-Q4_K_M-GGUF, Agents-A1-Q8_0-GGUF. This profile currently has 8 of 253 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Instruction Following (#5). This performance profile makes it a well-rounded choice across a range of tasks.
Ranking Distribution
Category rank across 4 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
Reasoning
Knowledge
Math
Multilingual
Multimodal
Inst. Following
#5Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how Agents-A1 stacks up against similar models
Frequently Asked Questions
How does Agents-A1 perform overall in AI benchmarks?
Agents-A1 has 8 published benchmark scores on BenchLM, but it does not yet have enough non-generated coverage to receive a global overall rank.
Is Agents-A1 good for knowledge and understanding?
Agents-A1 has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is Agents-A1 good for coding and programming?
Agents-A1 has visible benchmark coverage in coding and programming, but BenchLM does not currently assign it a global category rank there.
Is Agents-A1 good for reasoning and logic?
Agents-A1 has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Agents-A1 good for agentic tool use and computer tasks?
Agents-A1 has visible benchmark coverage in agentic tool use and computer tasks, but BenchLM does not currently assign it a global category rank there.
Is Agents-A1 good for instruction following?
Agents-A1 ranks #5 out of 70 models in instruction following benchmarks with an average score of 90.8. It is among the top performers in this category.
Is Agents-A1 open source?
Yes, Agents-A1 is an open weight model created by InternScience, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Which sibling models are related to Agents-A1?
Agents-A1 belongs to the Agents-A1 family. Related variants on BenchLM include Agents-A1-F16-GGUF, Agents-A1-FP8, Agents-A1-Q4_K_M-GGUF, Agents-A1-Q8_0-GGUF.
Does Agents-A1 have full benchmark coverage on BenchLM?
Not yet. Agents-A1 currently has 8 published benchmark scores out of the 253 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of Agents-A1?
Agents-A1 has a context window of 262K, which determines how much text it can process in a single interaction.
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