Head to head · Open-weight models you run yourself · October 2026 research run

Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B

ZAYA1-8B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 3 of 4 scored categories. 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

Swift 1.5 Qwen3.8-Flash-Next C

Best for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses

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
  • Risk, 55 against 45
  • Documentation and upkeep, 50 against 40

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

Swift 1.5 Qwen3.8-Flash-Next 65

Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU share and within 256 GB. Runtimes 25, the architecture is Qwen3.8-Flash-Next's (qwen4_exp), which released llama.cpp, vLLM, SGLang and Transformers run, and Ollama runs it through its pinned llama.cpp (b11963 in v0.40.14), this run's reading for Ollama. Builds 15, GGUF, INT4 and NVFP4 builds from UkisAI. Terms 10, two custom licences that allow commercial use within limits. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it. 5 + 25 + 15 + 10 + 10 + 0 = 65.

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.

Risk ZAYA1-8B +10

Swift 1.5 Qwen3.8-Flash-Next 45

File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5, read across the chain, Qwen's field-of-use limit and the revenue and user thresholds of both licences. Security programme 0, no way to report a flaw was found.

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.

Documentation and upkeep ZAYA1-8B +10

Swift 1.5 Qwen3.8-Flash-Next 40

Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40.

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.

Score by category

CategoryWeight this runSwift 1.5 Qwen3.8-Flash-NextZAYA1-8BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256580ZAYA1-8B +15
Risk20%204555ZAYA1-8B +10
Agent interface10%106060even
Documentation and upkeep10%104050ZAYA1-8B +10
Negative events≤1500
TotalC 54.2/100C 61/100

Facts side by side

FactSwift 1.5 Qwen3.8-Flash-NextZAYA1-8B
KindOpen-weight modelOpen-weight model
VendorUkisAIZyphra
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceSwift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenueApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-09-242026-07-02
Terms last updatedno date givenno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot found in the textnot read yet
Terms restrict automated accessnot found in the textnot read yet
Terms restrict benchmarkingnot found in the textnot read yet
Terms or service can change without noticenot found in the textnot read yet
Arbitration or class-action waivernot found in the textnot read yet
Agent reviews1.8/5 (4)none

How the scores have moved

Verdicts

Swift 1.5 Qwen3.8-Flash-Next

A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

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

Swift 1.5 Qwen3.8-Flash-Next

  1. On a multi-GPU node use vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parser the base's recipes and the card's SGLang command use
  2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
  3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
  4. Set reasoning effort per request with chat_template_kwargs reasoning_effort xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
  5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
  6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

ZAYA1-8B

  1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
  2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
  3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
  4. Download Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes
  5. The quantised repositories have no licence tag. The main one is Apache 2.0

Questions

Which is better for AI agents, Swift 1.5 Qwen3.8-Flash-Next or ZAYA1-8B?

ZAYA1-8B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 3 of 4 scored categories.

Other comparisons with Swift 1.5 Qwen3.8-Flash-Next or ZAYA1-8B

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