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

BigBang-v1 vs Swift 1.5 Qwen3.8-27B

Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 1 of 4 scored categories. BigBang-v1 leads on risk. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

BigBang-v1The Endless Frontier (endless-frontier) · Open-weight model C54.4/100#83 of 100 open-weight models Swift 1.5 Qwen3.8-27BUkisAI · Open-weight model C61/100#49 of 100 open-weight models

Which one, for what

BigBang-v1 C

Best for Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.

Ahead on

  • Risk, 55 against 50

Watch for

BF16 is the only build from the publisher, needing about 86 GB

Swift 1.5 Qwen3.8-27B C

Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

Ahead on

  • Reach, 80 against 55

Watch for

Commercial use above US$1M gross annual revenue needs a separate licence

Score by category

CategoryWeight this runBigBang-v1Swift 1.5 Qwen3.8-27BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255580Swift 1.5 Qwen3.8-27B +25
Risk20%205550BigBang-v1 +5
Agent interface10%106060even
Documentation and upkeep10%104643BigBang-v1 +3
Negative events≤1500
TotalC 54.4/100C 61/100

Facts side by side

FactBigBang-v1Swift 1.5 Qwen3.8-27B
KindOpen-weight modelOpen-weight model
VendorThe Endless Frontier (endless-frontier)UkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base
Read-only variant documentednono
llms.txtnono
Last release2026-08-062026-09-24
Terms last updatedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews2.3/5 (4)2.3/5 (4)

Verdicts

BigBang-v1

A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.

Swift 1.5 Qwen3.8-27B

A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

Before you call either

BigBang-v1

  1. On the reference machine use vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  2. The card says to keep at least 128K of context so its thinking isn't cut short
  3. Thinking control isn't documented, try the base template's enable_thinking false in chat_template_kwargs and check the output
  4. For a smaller build you'll need someone else's quantisation, the publisher has none
  5. Validate JSON output yourself or use the runtime's guided decoding

Swift 1.5 Qwen3.8-27B

  1. Quickest start is llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector
  2. On our reference machine BF16 fits, vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  3. The template is Qwen3.8-27B's, so chat_template_kwargs with reasoning_effort low, medium or xhigh (default) and enable_thinking false work as on the base
  4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
  5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
  6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
  7. MTP self-speculative decoding is available with the base's head, flags on the card

Questions

Which is better for AI agents, BigBang-v1 or Swift 1.5 Qwen3.8-27B?

Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 1 of 4 scored categories. BigBang-v1 leads on risk.

Other comparisons with BigBang-v1 or Swift 1.5 Qwen3.8-27B

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