Top AI Models for Vibe Coding (LATEST Tier List)

TL;DR

• The useful result is not one universal winner. Ron’s tested workflow separates planning from execution and combines models by job. (source video cReA64gDj4Y, 00:43)
• Keep Fable 5 in plan mode for review, feedback, and specifications; Ron’s hard rule in this video is not to let it write code. (source video cReA64gDj4Y, 01:45)
• For lower-cost implementation, Ron routes Fable’s spec to Kimi 2.7, MiniMax M3, DeepSeek V4 Pro, Qwen 3.7 Plus, or DeepSeek V4 Flash. Their strength here is instruction-following, not unsupervised long-horizon work. (source video cReA64gDj4Y, 04:30; 05:00)
• GLM 5.2 is Ron’s S-tier continuation model; Opus 4.8, Grok 4.5, and Qwen 3.7 Max occupy A tier for different jobs. (source video cReA64gDj4Y, 08:38; 10:24; 12:19; 13:49)
• Treat every rank as a July 13, 2026 workflow snapshot. Several judgments in the video depend on temporary model behavior, and one ranked model had not been tested by Ron. (source video cReA64gDj4Y, 06:19; 14:47)

Ron’s verdict

Do not ask one model to own every commit, and do not expect a cheap executor to invent the whole product. Start with Fable 5 as the planner, hand its specification to the right execution model, then bring the updated repository back for the next planning pass. That model route is the real S-tier result here. The letter beside a model matters less than whether it is planning, coding, researching, or tagging. (source video cReA64gDj4Y, 04:25; 05:21)

Key moments

• 00:00 — Why this tier list is different: real project iterations and model combinations replace a raw-capability-only ranking.
• 01:01 — Fable 5’s S+ caveat: high-end planning, poor coding, and the plan-mode rule.
• 04:25 — The planner-executor handoff: five cheaper models enter the B-tier execution pool.
• 06:19 — GPT 5.6 Soul’s unstable rank: Ron moves it between S and A based on the reasoning behavior he observed.
• 08:38 — Why GLM 5.2 reaches S tier: strong website work and better continuation of a plan.
• 12:19 — Grok 4.5’s specialist job: X-native research orchestration and opinion filtering.
• 15:11 — Gemini 3.5 Flash for transcripts: a narrow YouTube workflow beats asking it to reason deeply.
• 18:00 — The workflow in one line: all roads return to Fable 5 for the next specification.

Useful quotes

“But also, the key takeaway here is it’s not just one model that is king of all. There’s actually many different combinations that you can use to achieve the outcome that you want.” — Ron, source video cReA64gDj4Y, 00:40

“So, the most amount of value that you can get from Fable 5 is to keep it in plan mode. Don’t let it touch any code.” — Ron, source video cReA64gDj4Y, 01:45

“I find that their strength is purely following instructions.” — Ron, source video cReA64gDj4Y, 05:07

“all roads lead to Fable 5 right now.” — Ron, source video cReA64gDj4Y, 18:02

The tier list is really a routing table

The video ranks 23 models, but Ron’s evidence is job-shaped. Long-horizon work means a task that continues across many steps or iterations without losing the original goal. His B-tier executors can follow a strong specification well, but he says they drift when they must own that long arc themselves. (source video cReA64gDj4Y, 00:06; 05:00)

Job	Ron’s pick in this video	Why it goes there
Product and feature planning	Fable 5, S+	It produced detailed game progression, risk/reward, and early- versus late-game analysis; keep it away from code. (source video cReA64gDj4Y, 01:01; 02:00)
Continue a strong spec with less replanning	GLM 5.2, S	Ron calls it the best model here at continuing Fable’s work and praises its text-only website output. (source video cReA64gDj4Y, 08:46; 08:52; 10:18)
Execute a detailed spec cheaply	Kimi 2.7, MiniMax M3, DeepSeek V4 Pro, Qwen 3.7 Plus, or DeepSeek V4 Flash, B	Strong instruction-following; weaker ownership of long-horizon work. (source video cReA64gDj4Y, 04:30; 05:00)
Claude Code continuation	Opus 4.8, A	Ron keeps it as the ecosystem-aligned default for following Fable’s specs. (source video cReA64gDj4Y, 10:31; 10:34)
X-heavy news research	Grok 4.5, A	Ron uses it as a research-pipeline orchestrator to combine tagged accounts with useful opinions. (source video cReA64gDj4Y, 12:52; 13:07; 13:17)
Coding and terminal one-shots	Qwen 3.7 Max, A	Ron prefers Max over Plus for coding and terminal work; Plus is framed as more multimodal. (source video cReA64gDj4Y, 14:12; 14:28; 14:38)
Transcript tagging and timestamps	Gemini 3.5 Flash, B	It replaced MiniMax 2.5 in Ron’s YouTube workflow; he recommends straightforward marking work, not difficult reasoning. (source video cReA64gDj4Y, 15:52; 16:07)
Simple tagging	GPT 5.6 Luna, B	Ron uses it for direct tagging work and avoids it for reasoning. (source video cReA64gDj4Y, 08:22; 08:34)

This table is not permission to treat every label as a benchmark. Ron says Muse Spark was the only model in the set he had not tried, then gives it a provisional B based on mixed community feedback and a rough comparison. That is not equivalent to the project evidence behind the planner-executor workflow. (source video cReA64gDj4Y, 14:48; 14:53; 15:04)

The iteration loop worth copying

The practical technique is replanning at checkpoints, not asking one model to carry the whole build:

1. Give Fable 5 the current repository and ask for the next feature specification in plan mode. (source video cReA64gDj4Y, 04:14; 05:24)
2. Hand that specification to an execution model. Use a B-tier option when the instructions are strong and cost matters; choose GLM 5.2 or another higher-tier continuation model when you need more independent follow-through. (source video cReA64gDj4Y, 04:50; 10:03)
3. Commit the iteration, pull the updated repository, and return it to Fable 5 before planning the next feature. Ron says he made this his habit from July 8. (source video cReA64gDj4Y, 05:21; 05:49)
4. Do not let the cheaper executor silently become the planner for later iterations. Ron’s before-and-after comparison is that this is where the project drifted. (source video cReA64gDj4Y, 05:59)

That is a tiered model workflow: spend intelligence where it changes the architecture, then buy cheaper tokens for well-scoped execution. The video does not provide prices, token counts, or a universal success rate for this route, so this companion does not manufacture them.

Choose by failure mode

• Your build loses direction after several commits: return the current repository to Fable 5 for a new spec instead of extending the original plan indefinitely. (source video cReA64gDj4Y, 04:14; 06:02)
• You have a detailed spec and mostly need implementation: choose one of Ron’s B-tier executors; supervise the boundaries because he observed long-horizon drift. (source video cReA64gDj4Y, 04:30; 05:00)
• You need the executor to carry more of the next iteration: start with GLM 5.2, which Ron places above the other options for continuing Fable’s work. (source video cReA64gDj4Y, 10:18)
• Your job is research over X rather than coding: route it to Grok 4.5 instead of treating the coding tier as universal. (source video cReA64gDj4Y, 12:52)
• Your job is transcript marking or basic tags: use the narrow Gemini 3.5 Flash or Luna role described above; do not pay for planning strength you are not using. (source video cReA64gDj4Y, 08:22; 16:07)

What changed since this video

The video was published July 13, 2026, and this companion was source-checked against its saved transcript and timestamp segments on July 17, 2026. No outside pricing page, release note, benchmark, or current model test was added. The video’s claim that GPT 5.6 Soul had reduced reasoning and might recover “in a few weeks” is preserved only as Ron’s July 13 observation, not a current-state fact. (source video cReA64gDj4Y, 06:38; 07:05) The same boundary applies to every tier: use this as a dated workflow record, then test the exact model version available to you.

Related

• What an AI model tier list can and cannot tell you
• Cost-efficient models: pick by completed work, not raw rank
• Frontier vs open-weight: a workload decision framework