r/codex · past month

Gemini 4

Community sentiment from 33 posts · 170 comments · collected October 7, 2026 · generated October 7, 2026

Gemini 4 scores 0/100 overall in the r/codex community benchmark, collected October 7, 2026 from 33 posts and 170 comments. Its strongest category is Price / value at 62/100, and its weakest is Coding quality at 45/100.

0/ 100

Confidence: low — Coverage: 33 posts mentioning Gemini 4 (smallest sample in the ranking) from a single subreddit (r/codex) inside the window the archive labels month, with up to 40 kept comments per post across the 10 highest-scored posts — 170 kept comments in total. The model is on limited release ('aka not public yet'), so nearly every statement is second-hand or speculative; instruction-following, reliability and creative-writing statements are absent, so those categories are null on purpose. What this is not: an independent measurement.

Category scores

Intelligence

55

Hopeful but hedged: 'seems solid. or benchmaxxed. or both', the one hard number is 'the hallucination rate on argon apparently is sub 15% which insanely good', and 'Can Gemini 4 be our saviour' is literally a question. It's limited-release ('aka not public yet'), so nobody in the sample has run it.

Speed

55

Family reputation only: 'Gemini 3.8 flash is its flashy fast and also makes mistakes like theres no tommorow. I need Sol to fix its implementation. This Argon might change all that. Waiting...'

Coding quality

45

Judged by the 3.8 Flash experience — fast but 'makes mistakes like theres no tommorow' — and the Google track-record complaint: 'Google have really dropped the ball with basically nothing coming out after I/O.'

Instruction following

no data

No data in the sample.

Long context

50

Only a suggestion exists: 'Increase the context window to like 1mil tokens and you'd see much better results' in the writing thread — a wish, not a measurement.

Price / value

62

The small Gemini subscription is conceded as real value: 'I didn't realise the small sub for Gemini could really get you that much usage... if you are talking pure value I don't think you are going to find anything better than that', though 'the quality will be worse than DeepSeek'.

Reliability & uptime

no data

No data in the sample — the sub-15% hallucination-rate chatter is the closest signal and stays unverified.

Creativity & writing

no data

No data in the sample — the writing thread's answers favor Astra and 5.6 Sol; Gemini is never endorsed for prose.

Multimodal & vision

50

History, not current product: 'Google have really dropped the ball... They had nano banana for a while and then chatgpt images took that crown too' — Google's image-gen lead is acknowledged as lost.

Agentic & tool use

50

Appears only as plumbing in multi-agent workflows — the secondary/burner-agent threads list 'Claude, Codex, Cursor, Gemini CLI, Copilot, OpenCode' — never as the agent anyone runs first.

Summary

The sample is 10 posts from r/codex inside the window the archive labels month, with up to 40 kept comments per post (170 kept in total), and it is the thinnest of the eight — fitting for a model on 'limited release right now, aka not public yet'. The top thread asks the whole question: 'Can Gemini 4 be our saviour'. The hopeful answers cite the one hard number circulating: 'the hallucination rate on argon apparently is sub 15% which insanely good', while the skeptics answer with the brand's recent record — 'Google have really dropped the ball with basically nothing coming out after I/O. They had nano banana for a while and then chatgpt images took that crown too.'

The middle ground is the most representative: 'oh damn. i mean. seems solid. or benchmaxxed. or both. I'd love to see a good gemini though, yeah we need more alternatives than anthropic and openai, the more competition the better for users.' Gemini 4's role in the sample is the third-competitor hypothesis — nobody runs it, everyone wants it to be real.

Where Gemini actually appears in workflows is plumbing: the secondary-agent and burner-agent threads list it alongside 'Claude, Codex, Cursor, Gemini CLI, Copilot, OpenCode', and the small Gemini subscription gets conceded as genuine value — 'I didn't realise the small sub for Gemini could really get you that much usage... if you are talking pure value I don't think you are going to find anything better than that' — with the immediate caveat that 'the quality will be worse than DeepSeek'.

Everything here is archive-derived sentiment from a self-selected Reddit sample of a model most commenters have not used: no independent measurements, and the sub-15% hallucination figure is unverified.

Pros and cons

Pros

  • The one hard number in the sample is flattering: 'the hallucination rate on argon apparently is sub 15% which insanely good.'
  • The small Gemini subscription is conceded as genuine value: 'if you are talking pure value I don't think you are going to find anything better than that.'
  • Third-competitor demand is real and Gemini is its default beneficiary: 'we need more alternatives than anthropic and openai, the more competition the better for users.'

Cons

  • Nobody in the sample has actually run it: 'It's out on limited release right now, aka not public yet' — every judgment is second-hand or speculative.
  • Judged by the 3.8 Flash reputation: 'flashy fast and also makes mistakes like theres no tommorow. I need Sol to fix its implementation.'
  • The Google track record weighs on it: 'Google have really dropped the ball with basically nothing coming out after I/O.'
  • Benchmark trust is preemptively low: 'seems solid. or benchmaxxed. or both.'

Top quotes

  • “You mean like a bait and switch? Nah. Corporations would never do that.”
    skynet86 · 53 points neutral view on Reddit ↗
  • “I've been saying this shit for most of this year now. The best time for OAI to take a sizeable lead over Anthropic in the coding/enterprise sector was last year. Early to mid last year. Now Anthropic has made like $400-500 billion in multi-year compute deals just this year. They have probably 80% of the compute that OAI has with a fraction of the user base. Which means far far more revenue per user than OAI. It also means they can dedicate way more compute to training. Couple that with being in a far better financial situation than OAI. Anthropic is essentially geared to play the long game. OAI's far higher marketshare is great.......if you can convert it to dollars. The resets? Also great; if you have the compute to keep them up. WHILE keeping usage limits the same or better. Neither are possible unfortunately.”
    randombsname1 · 17 points negative view on Reddit ↗
  • “Argon is a gas tho (unless its 189.34° C)”
    MedicalTear0 · 8 points neutral view on Reddit ↗
  • “oh damn. i mean. seems solid. or benchmaxxed. or both. I'd love to see a good gemini though, yeah we need more alternatives than anthropic and openai, the more competition the better for users”
    Master-Shift-8224 · 5 points positive view on Reddit ↗
  • “Most exciting thing is the hallucination rate on argon apparently is sub 15% which insanely good”
    lordpuddingcup · 5 points positive view on Reddit ↗
  • “I hope so. Google have really dropped the ball with basically nothing coming out after I/O. They had nano banana for a while and then chatgpt images took that crown too.”
    MessyBookPilesFTW · 3 points negative view on Reddit ↗
  • “One thing i noticed with Gemini 3.8 flash is its flashy fast and also makes mistakes like theres no tommorow. I need Sol to fix its implementation. This Argon might change all that. Waiting...”
    No-Temperature6597 · 2 points negative view on Reddit ↗
  • “Google taking the lead would be a plot twist”
    CthuluBob · 6 points neutral view on Reddit ↗

Top sample posts

Top 5 of 33 posts by score

FAQ

What is Gemini 4's overall community score?
0 out of 100, with low confidence: Coverage: 33 posts mentioning Gemini 4 (smallest sample in the ranking) from a single subreddit (r/codex) inside the window the archive labels month, with up to 40 kept comments per post across the 10 highest-scored posts — 170 kept comments in total. The model is on limited release ('aka not public yet'), so nearly every statement is second-hand or speculative; instruction-following, reliability and creative-writing statements are absent, so those categories are null on purpose. What this is not: an independent measurement.
How was the Gemini 4 benchmark collected?
From 33 posts and 170 comments on r/codex sampled over the past month window.
Which categories does the Gemini 4 report score?
Intelligence, Speed, Coding quality, Instruction following, Long context, Price / value, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.
What are the main community pros and cons for Gemini 4?
Top pros: The one hard number in the sample is flattering: 'the hallucination rate on argon apparently is sub 15% which insanely good.'; The small Gemini subscription is conceded as genuine value: 'if you are talking pure value I don't think you are going to find anything better than that.'. Main cons: Nobody in the sample has actually run it: 'It's out on limited release right now, aka not public yet' — every judgment is second-hand or speculative.; Judged by the 3.8 Flash reputation: 'flashy fast and also makes mistakes like theres no tommorow. I need Sol to fix its implementation.'.
When was the Gemini 4 data collected?
Collected on October 7, 2026; the report was generated on October 7, 2026.

Collected from public r/codex posts and comments via Reddit JSON API on October 7, 2026.

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