Comment by mattlondon
15 hours ago
Currently top at https://deepswe.datacurve.ai - beating Opus 5!
https://artificialanalysis.ai/models/gemini-3-8-flash shows an intelligence score of 59, the same as Opus 5 medium!
Wow - for a flash model this seems to benchmark powerfully. Remains to be seen what it is like to use.
I've been trying this Gemini 3.8 Flash for a day. Looks not much different than Gemini 3.7 Flash in my use case: I have Codex (gpt-5.6 sol) write up a design plan to implement a feature or refactor a portion of a system I am building, and have Claude (Opus-5) and Gemini (3.8 Flash) review and critique the plan, until all problems are addressed by Codex and approved by the reviewers; then have a cheaper model of Codex (gpt-5.6 luna) implement the plan, and still have Claude (Opus-5) and Gemini (3.8 Flash) review and critique the implementation, until all problems are addressed by Codex and approved by the reviewers.
The result is the same as the previous Gemini 3.6/3.7 Flash days: Claude could always note much more problems in Codex's plan and implementation than Gemini could - the ratio is like 10:1.
I occasionally switch the roles between Codex and Claude, and result is the same, Codex could always catch much more problems in Claude's plan and implementation, than Gemini could.
So I am guessing in a relatedly complex codebase, Gemini is much less effective in acting as a guardrail (or a senior engineer/team lead) than the other SOTA models.
As of writing this comment, Claude Opus 5 has an intelligence score of 63, not 59 (it's not the same as Gemini 3.8 Flash).
With a score of 59, Gemini 3.8 Flash is in eighth place, falling behind even Grok 4.6, Kimi k3, and GLM 5.3.
https://imgur.com/a/BMOJBED
They said Opus 5 medium - which does have an intelligence score of 59 (you have to select it manually from the dropdown to see it)
They are all much larger and more expensive models. Google does not have a frontier model right now, but for cheap ones, they are better than event the chinese models now.
That's not being debated here. The initial reported numbers were false and this was simply pointed out. You're changing the subject.
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Better than even the Chinese models? That's a difficult-to-quantify, extremely rapidly moving target. Just today, Qwen 3.8 Max 0902 came out with a huge improvement over the previous Qwen 3.8 Max.
That 63 score is for Max. The OP specified medium.
On artificial analysis it's only equal to opus 5 medium effort. Opus 5 max scores 63.
Further, opus 5 medium outputs 4x fewer tokens to achieve the same result, negating a lot of the speed difference.
A comparison to an artificial score and a comparison to “the same task”
These folks must laugh themselves to sleep. This whole industry hoodwinked the masses. It’s impressive.
It’s all just vibes
The benchmark also doesn't include speed. You almost think something has gone wrong when using it because it returns full responses so incredibly fast.
Not just speed, also reliability. IME, Gemini's speed and quality doesn't degrade badly during weekday working hours compared to OAI, and especially Anthropic.
I've had Gemini model API use degrade the most out of OAI/Anthropic/Google (often "over capacity" vs true failures)
Not sure on consumer/product use though
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This one uses that as a priority weight: https://winstonrc.github.io/ai-coding-agents-leaderboard/
The rumor is that 3.9 is an equal improvement in all directions, and that it should be another fast follow on like 3.7 and 3.8 were.
It's almost across the board better than Terra at less than half the price. 3.9 is likely to approach Sol at the 1/10th the price.
Hopefully OpenAI releases Astra first, and it's not only better than Sol but significantly cheaper, too.
Curious where did you hear this rumor?
All the talk on Reddit on Gemini 3.8 discussions: https://www.reddit.com/search/?q=gemini+3.9&cId=1650e403-bcf...
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A fifth of the cost of Opus 5! Google is certainly pushing the completion with this.
Gemini hasn't failed me for personal usage yet. I haven't had the opportunity to use it at work.
I've been using 3.7 Flash to audit the work of Opus High, and Flash finds lots of subtle and insidious defects even while all the unit tests are green.
Then I tell Opus to read the audit report and implement what it agrees with.
Flash is really good at this, and it is blazing fast in Antigravity CLI. Easily 10x faster than Opus.
Can't wait to try 3.8 Flash. If it's good enough, maybe I'll switch Flash to primary and make Opus the auditor.
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Idk, was building/maintaining simple esp32 control program with antig/opus. After last update it defaulted to gflash3.7. I pasted an email requesting 2 changes into the chat prompt, it did one and took me 4 turns to get that one right.
Crushing it on DeepSWE is a very big deal. Excited to give this a try.
I know everyone is benchmaxxing but this one feels one step too far. Doesn't DeepSWE have both public and private tasks? I'd love to see the diff here.
It looks more like Google execs losing their mind and pressuring researchers to put DeepSWE directly into the training set.
> DeepSWE is a very big deal
It's clearly been "dealt with" already. When it launched we had interesting gaps and definitely differences. Now every new release is "crushing it".
Will look forward to the "feel" of the model in real testing. But I agree that these benchmarks do get "dealt with" rapidly. That's a shame, but I guess it's the times we live in.
Google - we're so back
Only 1 point behind the Chinese SOTA from two months ago.
If you care about points sure, but personnaly I care about price, performance, speed and reliability
I had qwen 3.8 3bit model drop into chinese on long runs. I had to remind it to use english. Its still better than every gemma model I tried. Gemma deleted files on a harddrive to make space when there was over 2TB free. For long runs, gemma is useless.
deepswe is public and can be considered contaminated.
>shows an intelligence score of 59, the same as Opus 5!
...on Medium reasoning. Claude Opus 5 (high) is the default in e.g. Claude Code and scores 61. Still very impressive.
We'll see about that. I suspect benchmaxxing as all the labs do as I haven't found Gemini models to be nearly as good in agentic engineering compared to Claude or GPT models.
If anything, gemini models are the least benchmaxxed out of any lab, IMO.
And the benchmarks agreed with you... until now.
So, yes, maybe it's still not - but this would be the only time it would be highly suspicious / obvious benchmaxxing / obviously bad benchmarks.
There are important gaps in that hot take.
For example, it's not even close to Opus 5 on Terminal-bench 4.0, 19.1% vs. 51.8%.
sidenote, but wow sonnet 5 is shockingly bad on this benchmark.
sonnet 5 is bad by almost any metric.
anthropic really needs something to address the cheaper end of the market before they get left behind. Sonnet 5 sucks, and Haiku hasn't been updated in a year. meanwhile we've got gemini flash, luna, and GLM5.3 all delivering 90% of the performance for a small fraction of the cost. paying $25/mTok is going to start looking pretty silly soon.
Wait a week with your judgement - most likely, Google is just bench-maxing very hard. If you look at the previous Flash models and the announcement on Google I/O, it was an absolute disaster. Reality diverged very much from the marketing (supposedly great benchmarks).