Head to head · Open-weight models you run yourself · October 2026 research run
Cosmos-Reason2-2B vs ZAYA1-8B
ZAYA1-8B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 2 of 4 scored categories. Cosmos-Reason2-2B leads on risk and agent interface. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces
Ahead on
- Risk, 60 against 55
- Agent interface, 65 against 60
Watch for
Gated behind a click-through licence that needs a Hugging Face account
ZAYA1-8B C
Best for People who want a small, fast reasoning model for maths and code and can live without tool calling
Ahead on
- Reach, 80 against 65
- Documentation and upkeep, 50 against 39
Watch for
Tool calling needs a parser that only Zyphra's vLLM fork has
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach ZAYA1-8B +15
Cosmos-Reason2-2B 65
Memory earns the full 30 because the BF16 file is 4.9 GB, about 5.9 GB with the fifth added, though NVIDIA itself asks for 24 GB of GPU memory. Runtimes get 20 because the model keeps Qwen3-VL's architecture, which Transformers, vLLM, SGLang and llama.cpp run in released versions, while LM Studio's catalogue has only the base. Builds score 0, as NVIDIA publishes no quantised build of the 2B. The NVIDIA Open Model License allows free commercial use under a use policy (10). The gate is an automatic click-through that needs an account (5), and nothing hosts the 2B (0).
ZAYA1-8B 80
Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.
Documentation and upkeep ZAYA1-8B +11
Cosmos-Reason2-2B 39
The card misses languages and a cut-off (14). Training data is named by dataset without sizes or a cut-off (5). The Hub repository last changed on 30 April 2026, 188 days before this check, which alone gives 5. NVIDIA's GitHub repository last changed with a README redirect to Cosmos 3 on 7 June 2026, 150 days back, and this run counts the publisher's documentation commits on this line (10). None of its 3 discussions has an NVIDIA reply, counted by author flags on 4 November (0). The 2B's weights were replaced in place in March with a dated line, and the family is now superseded by Cosmos 3 with a dated note (5). The gated files couldn't be checked against the card, which itself mixes float16 and BF16 and links a newer licence than the gate shows (5). 14 + 5 + 10 + 0 + 5 + 5 = 39.
ZAYA1-8B 50
Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.
Risk Cosmos-Reason2-2B +5
Cosmos-Reason2-2B 60
File safety gets the full 25, one safetensors file loaded by Transformers' own code with a template, no remote code. Provenance gets 15 for files in NVIDIA's own organisation with hashes. The licence has a use policy and guardrail clause and NVIDIA can revoke or update it, so licence risk keeps 5 of 15. NVIDIA's PSIRT route and bulletins give the security programme 10. Agent guidance gets 5 for general cautions about guardrails and physical risk, and safety evaluation 0, with no safety results.
ZAYA1-8B 55
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Agent interface Cosmos-Reason2-2B +5
Cosmos-Reason2-2B 65
A Jinja template ships as chat_template.json (20). Tools are declared only by the template it inherits from Qwen3-VL, with no NVIDIA documentation or parser (10), and nothing shows the 2B was trained for tool use (0). NVIDIA's prompt files ask for JSON with named keys for boxes, events and trajectories (15). Reasoning is asked for in the prompt and vLLM's qwen3 parser returns it apart (10). The card states 256K input tokens (10).
ZAYA1-8B 60
Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.
Score by category
| Category | Weight this run | Cosmos-Reason2-2B | ZAYA1-8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 80 | ZAYA1-8B +15 |
| Risk | 20%20 | 60 | 55 | Cosmos-Reason2-2B +5 |
| Agent interface | 10%10 | 65 | 60 | Cosmos-Reason2-2B +5 |
| Documentation and upkeep | 10%10 | 39 | 50 | ZAYA1-8B +11 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.5/100 | C 61/100 |
Facts side by side
| Fact | Cosmos-Reason2-2B | ZAYA1-8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Zyphra |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-10 | 2026-07-02 |
| Terms last updated | 2025-10-24 | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 2.5/5 (4) | none |
Verdicts
Cosmos-Reason2-2B
A small local model for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.
ZAYA1-8B
A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.
Before you call either
Cosmos-Reason2-2B
- Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading
- Serve it with
vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{"video": {"num_frames": -1}}' --reasoning-parser qwen3, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises - For local video or images, make a folder for them (
mkdir -p media) and pass--allowed-local-media-path "$(pwd)/media", so the server can read only that folder, not everything under the directory you start it from - Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off
- For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply
- Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself
- Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks
ZAYA1-8B
- Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
- Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
- Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
- Download
Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes - The quantised repositories have no licence tag. The main one is Apache 2.0
Questions
Which is better for AI agents, Cosmos-Reason2-2B or ZAYA1-8B?
ZAYA1-8B scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 2 of 4 scored categories. Cosmos-Reason2-2B leads on risk and agent interface.
Other comparisons with Cosmos-Reason2-2B or ZAYA1-8B
- Agents-A1-4B vs Cosmos-Reason2-2B
- Agents-A1-4B vs ZAYA1-8B
- Apertus v1.5 8B vs Cosmos-Reason2-2B
- Apertus v1.5 8B vs ZAYA1-8B
- Apodex-1.1-mini vs Cosmos-Reason2-2B
- Apodex-1.1-mini vs ZAYA1-8B
- Arete-27B vs Cosmos-Reason2-2B
- Arete-27B vs ZAYA1-8B
- BigBang-v1 vs Cosmos-Reason2-2B
- BigBang-v1 vs ZAYA1-8B
- Carbon-X vs Cosmos-Reason2-2B
- Carbon-X vs ZAYA1-8B
- Cola2 vs Cosmos-Reason2-2B
- Cola2 vs ZAYA1-8B
- Cosmos-Reason2-2B vs Darwin-36B-Opus
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs DeepSeek V4.1 Flash
- Cosmos-Reason2-2B vs DeepSeek-V4-Flash-0731
- Cosmos-Reason2-2B vs Devstral Small 2
- Cosmos-Reason2-2B vs DiffusionGemma 26B A4B IT
- Cosmos-Reason2-2B vs EXAONE 4.5 33B
- Cosmos-Reason2-2B vs Gemma 4 12B IT
- Cosmos-Reason2-2B vs Gemma 4 26B A4B IT
- Cosmos-Reason2-2B vs Gemma 4 31B IT
- Cosmos-Reason2-2B vs Gemma 4 E2B IT
- Cosmos-Reason2-2B vs Gemma 4 E4B IT
- Cosmos-Reason2-2B vs GLM-4.5-Air
- Cosmos-Reason2-2B vs GLM-4.7-Flash
- Cosmos-Reason2-2B vs GLM-5.3
- Cosmos-Reason2-2B vs GLM-5.3-Flash
- Cosmos-Reason2-2B vs gpt-oss-120b
- Cosmos-Reason2-2B vs gpt-oss-20b
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Granite 4.2 3B
- Cosmos-Reason2-2B vs Granite 4.2 8B
- Cosmos-Reason2-2B vs Hy-Memory-30B-A3B
- Cosmos-Reason2-2B vs Hy3
- Cosmos-Reason2-2B vs Inkling
- Cosmos-Reason2-2B vs Inkling-Small
- Cosmos-Reason2-2B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Cosmos-Reason2-2B vs Kimi K3
- Cosmos-Reason2-2B vs Kimi K3.1 Pro
- Cosmos-Reason2-2B vs Kolibri-1
- Cosmos-Reason2-2B vs Kronos
- Cosmos-Reason2-2B vs Laguna M.1
- Cosmos-Reason2-2B vs Laguna S 2.1
- Cosmos-Reason2-2B vs Laguna XS 2.1
- Cosmos-Reason2-2B vs LBS-27B
- Cosmos-Reason2-2B vs LFM2.5-2.6B
- Cosmos-Reason2-2B vs LFM3-1.7B
- Cosmos-Reason2-2B vs LFM3-Audio-2B
- Cosmos-Reason2-2B vs LFM3-Omni-2B
- Cosmos-Reason2-2B vs LFM3-VL-2B
- Cosmos-Reason2-2B vs Ling-3.0-tiny
- Cosmos-Reason2-2B vs Ling-3.1-flash-1019
- Cosmos-Reason2-2B vs LongCat-2.5
- Cosmos-Reason2-2B vs MiMo-V2.6 Distill Qwen 9B
- Cosmos-Reason2-2B vs MiMo-V2.6-Flash-RL
- Cosmos-Reason2-2B vs MiniCPM-o 4.5
- Cosmos-Reason2-2B vs MiniCPM-V 4.7 1B
- Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B
- Cosmos-Reason2-2B vs MiniCPM5-2B
- Cosmos-Reason2-2B vs MiniMax-M2.5
- Cosmos-Reason2-2B vs MiniMax-M2.7
- Cosmos-Reason2-2B vs MiniMax-M3.1-Flash
- Cosmos-Reason2-2B vs Mistral Large 4
- Cosmos-Reason2-2B vs Muse Glimmer 30B
- Cosmos-Reason2-2B vs Nanbeige4.2-3B
- Cosmos-Reason2-2B vs Nanbeige4.3-33B
- Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B
- Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B
- Cosmos-Reason2-2B vs Nemotron 3 Nano 4B
- Cosmos-Reason2-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Cosmos-Reason2-2B vs Nemotron 3 Super 120B A12B
- Cosmos-Reason2-2B vs Nemotron 3 Ultra 550B A55B
- Cosmos-Reason2-2B vs North Mini Code 1.0
- Cosmos-Reason2-2B vs occamy-1.0
- Cosmos-Reason2-2B vs Olmo 3 7B Think
- Cosmos-Reason2-2B vs Ornith-1.5-122B
- Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B
- Cosmos-Reason2-2B vs Ornith-1.5-397B
- Cosmos-Reason2-2B vs Ornith-1.5-9B
- Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT
- Cosmos-Reason2-2B vs Qwen-AgentWorld-35B-A3B
- Cosmos-Reason2-2B vs Qwen3-30B-A3B-Instruct-2507
- Cosmos-Reason2-2B vs Qwen3-4B-Instruct-2507
- Cosmos-Reason2-2B vs Qwen3.5-0.8B
- Cosmos-Reason2-2B vs Qwen3.5-122B-A10B
- Cosmos-Reason2-2B vs Qwen3.5-2B
- Cosmos-Reason2-2B vs Qwen3.5-397B-A17B
- Cosmos-Reason2-2B vs Qwen3.5-4B
- Cosmos-Reason2-2B vs Qwen3.5-9B
- Cosmos-Reason2-2B vs Qwen3.6-27B
- Cosmos-Reason2-2B vs Qwen3.6-35B-A3B
- Cosmos-Reason2-2B vs Qwen3.8-27B
- Cosmos-Reason2-2B vs Qwen3.8-Flash-Next
- Cosmos-Reason2-2B vs Qwen3-Coder-30B-A3B-Instruct
- Cosmos-Reason2-2B vs Qwen3-Coder-Next
- Cosmos-Reason2-2B vs Qwen3-Omni-30B-A3B-Instruct
- Cosmos-Reason2-2B vs Qwen3-VL-2B-Instruct
- Cosmos-Reason2-2B vs Qwen3-VL-30B-A3B-Instruct
- Cosmos-Reason2-2B vs Qwen3-VL-32B-Instruct
- Cosmos-Reason2-2B vs Qwen3-VL-4B-Instruct
- Cosmos-Reason2-2B vs Qwen3-VL-8B-Instruct
- Cosmos-Reason2-2B vs Qwopus3.8-27B-Flash-V2
- Cosmos-Reason2-2B vs Recursive Flint
- Cosmos-Reason2-2B vs Ripple-65B Preview
- Cosmos-Reason2-2B vs SenseNova-Loop-9B-Preview
- Cosmos-Reason2-2B vs SmolLM3-3B
- Cosmos-Reason2-2B vs Spark-X2.6-4B
- Cosmos-Reason2-2B vs Step 3.7 Flash
- Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-27B
- Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-Flash-Next
- Cosmos-Reason2-2B vs ThinkingCap (Qwen3.8 27B)
- Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2
- Cosmos-Reason2-2B vs WeVisOne-VL-2B
- Cosmos-Reason2-2B vs WeVisOne-VL-4B
- Darwin-36B-Opus vs ZAYA1-8B
- DeepSeek-V3.2 vs ZAYA1-8B
- DeepSeek V4.1 Flash vs ZAYA1-8B
- DeepSeek-V4-Flash-0731 vs ZAYA1-8B
- Devstral Small 2 vs ZAYA1-8B
- DiffusionGemma 26B A4B IT vs ZAYA1-8B
- EXAONE 4.5 33B vs ZAYA1-8B
- Gemma 4 12B IT vs ZAYA1-8B
- Gemma 4 26B A4B IT vs ZAYA1-8B
- Gemma 4 31B IT vs ZAYA1-8B
- Gemma 4 E2B IT vs ZAYA1-8B
- Gemma 4 E4B IT vs ZAYA1-8B
- GLM-4.5-Air vs ZAYA1-8B
- GLM-4.7-Flash vs ZAYA1-8B
- GLM-5.3-Flash vs ZAYA1-8B
- GLM-5.3 vs ZAYA1-8B
- gpt-oss-120b vs ZAYA1-8B
- gpt-oss-20b vs ZAYA1-8B
- Granite 4.2 30B vs ZAYA1-8B
- Granite 4.2 3B vs ZAYA1-8B
- Granite 4.2 8B vs ZAYA1-8B
- Hy-Memory-30B-A3B vs ZAYA1-8B
- Hy3 vs ZAYA1-8B
- Inkling-Small vs ZAYA1-8B
- Inkling vs ZAYA1-8B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B
- Kimi K3.1 Pro vs ZAYA1-8B
- Kimi K3 vs ZAYA1-8B
- Kolibri-1 vs ZAYA1-8B
- Kronos vs ZAYA1-8B
- Laguna M.1 vs ZAYA1-8B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs ZAYA1-8B
- LFM2.5-2.6B vs ZAYA1-8B
- LFM3-1.7B vs ZAYA1-8B
- LFM3-Audio-2B vs ZAYA1-8B
- LFM3-Omni-2B vs ZAYA1-8B
- LFM3-VL-2B vs ZAYA1-8B
- Ling-3.0-tiny vs ZAYA1-8B
- Ling-3.1-flash-1019 vs ZAYA1-8B
- LongCat-2.5 vs ZAYA1-8B
- MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B
- MiMo-V2.6-Flash-RL vs ZAYA1-8B
- MiniCPM-o 4.5 vs ZAYA1-8B
- MiniCPM-V 4.7 1B vs ZAYA1-8B
- MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B
- MiniCPM5-2B vs ZAYA1-8B
- MiniMax-M2.5 vs ZAYA1-8B
- MiniMax-M2.7 vs ZAYA1-8B
- MiniMax-M3.1-Flash vs ZAYA1-8B
- Mistral Large 4 vs ZAYA1-8B
- Muse Glimmer 30B vs ZAYA1-8B
- Nanbeige4.2-3B vs ZAYA1-8B
- Nanbeige4.3-33B vs ZAYA1-8B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ZAYA1-8B
- Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 4B vs ZAYA1-8B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ZAYA1-8B
- Nemotron 3 Super 120B A12B vs ZAYA1-8B
- Nemotron 3 Ultra 550B A55B vs ZAYA1-8B
- North Mini Code 1.0 vs ZAYA1-8B
- occamy-1.0 vs ZAYA1-8B
- Olmo 3 7B Think vs ZAYA1-8B
- Ornith-1.5-122B vs ZAYA1-8B
- Ornith-1.5-35B-A3B vs ZAYA1-8B
- Ornith-1.5-397B vs ZAYA1-8B
- Ornith-1.5-9B vs ZAYA1-8B
- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
- Qwen-AgentWorld-35B-A3B vs ZAYA1-8B
- Qwen3-30B-A3B-Instruct-2507 vs ZAYA1-8B
- Qwen3-4B-Instruct-2507 vs ZAYA1-8B
- Qwen3.5-0.8B vs ZAYA1-8B
- Qwen3.5-122B-A10B vs ZAYA1-8B
- Qwen3.5-2B vs ZAYA1-8B
- Qwen3.5-397B-A17B vs ZAYA1-8B
- Qwen3.5-4B vs ZAYA1-8B
- Qwen3.5-9B vs ZAYA1-8B
- Qwen3.6-27B vs ZAYA1-8B
- Qwen3.6-35B-A3B vs ZAYA1-8B
- Qwen3.8-27B vs ZAYA1-8B
- Qwen3.8-Flash-Next vs ZAYA1-8B
- Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-Coder-Next vs ZAYA1-8B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-2B-Instruct vs ZAYA1-8B
- Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-32B-Instruct vs ZAYA1-8B
- Qwen3-VL-4B-Instruct vs ZAYA1-8B
- Qwen3-VL-8B-Instruct vs ZAYA1-8B
- Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B
- Recursive Flint vs ZAYA1-8B
- Ripple-65B Preview vs ZAYA1-8B
- SenseNova-Loop-9B-Preview vs ZAYA1-8B
- SmolLM3-3B vs ZAYA1-8B
- Spark-X2.6-4B vs ZAYA1-8B
- Step 3.7 Flash vs ZAYA1-8B
- Swift 1.5 Qwen3.8-27B vs ZAYA1-8B
- Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B
- ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs ZAYA1-8B
- WeVisOne-VL-2B vs ZAYA1-8B
- WeVisOne-VL-4B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/cosmos-reason2-2b-vs-zaya1-8b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/cosmos-reason2-2b.json·/api/v1/tools/zaya1-8b.json - From a terminal
anchor compare cosmos-reason2-2b zaya1-8b(the CLI) - Over MCP
compare_tools {"a": "cosmos-reason2-2b", "b": "zaya1-8b"}at/mcp, no key