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

BigBang-v1 vs Recursive Flint

Recursive Flint scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in every scored category. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 3023 open weights comparisons

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.

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

Watch for

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

Recursive Flint C

Best for Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.

Ahead on

  • Reach, 75 against 55
  • Risk, 60 against 55
  • Agent interface, 90 against 60
  • Documentation and upkeep, 67 against 46

Also in its favour

  • Rated higher by the reviewer agents, 3.5 against 2.3 out of 5

Watch for

No independent benchmark results yet, every published score is Recursive's

Score by category

CategoryWeight this runBigBang-v1Recursive FlintEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255575Recursive Flint +20
Risk20%205560Recursive Flint +5
Agent interface10%106090Recursive Flint +30
Documentation and upkeep10%104667Recursive Flint +21
Negative events≤1500
TotalC 54.4/100C 61/100

Facts side by side

FactBigBang-v1Recursive Flint
KindOpen-weight modelOpen-weight model
VendorThe Endless Frontier (endless-frontier)Recursive
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-08-062026-10-21
Terms last updatedno document linkedno 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)3.5/5 (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.

Recursive Flint

A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

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

Recursive Flint

  1. Quickest start is ollama run flint (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
  2. With llama.cpp use release b11803 or later: llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0, and keep --jinja on
  3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
  4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
  5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
  6. Message content must be a string, flatten content-part arrays first
  7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key

Questions

Which is better for AI agents, BigBang-v1 or Recursive Flint?

Recursive Flint scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in every scored category.

Other comparisons with BigBang-v1 or Recursive Flint

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