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@cursor_ai
We use a combination of offline benchmarks and online evals to measure model quality. This makes results more useful, especially as public benchmarks are increasingly saturated.
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@cursor_aiposted on X
We're sharing a new method for scoring models on agentic coding tasks. Here's how models in Cursor compare on intelligence and efficiency: https://t.co/VItnifMh55
View original tweet on X →
Community Sentiment Analysis
Real-time analysis of public opinion and engagement
Sentiment Distribution
66% Engaged
50% Positive
16% Negative
Positive
50%
Negative
16%
Neutral
34%
Key Takeaways
What the community is saying — both sides
Supporting
1
useful, action-oriented
alternative to saturated leaderboards.
2
The dominant theme is the primacy of token efficiency — many argue a slightly-le...
The dominant theme is the primacy of token efficiency — many argue a slightly-less-smart but much cheaper/faster model beats a top-scoring but costly one for day-to-day coding.
3
GPT-5.4 (high) is repeatedly singled out for hitting a strong Pareto balance of ...
GPT-5.4 (high) is repeatedly singled out for hitting a strong Pareto balance of intelligence and token efficiency, with several users reporting it outperforms older variants.
4
Opus 4.6
, Composer, and GLM are praised for punching above their weight on the efficiency frontier.
5
speed
, multi-step/task complexity, and especially long-horizon context retention across multi-hour coding sessions are frequent requests.
6
Calls for broader coverage
many ask to include more models (Grok, Qwen, Kimi, Sonnet variants, GPT-5.4 modes) and to publish methodology or open-source the benchmark.
7
cost per solved
or task-price alongside token counts so teams can estimate real production spend.
8
Several replies note real-world signals (live usage, build/regression behavior, ...
Several replies note real-world signals (live usage, build/regression behavior, hallucinated imports) matter more than static scores — this framing makes the leaderboard feel more trustworthy.
9
The tone is largely enthusiastic and pragmatic
users are excited to see data that helps route tasks to the right model rather than chase prestige metrics.
Opposing
1
unreliable
and cherry‑picked, with users pointing to suspicious rankings (Sonnet, Opus, Composer) and complaining about sloppy chart design.
2
prohibitively expensive
(one user cited $26 for a prompt) and see efficiency scores as a literal price tag for layoffs.
3
Conversation shifts from “which model writes best” to “which model uses the fewe...
Conversation shifts from “which model writes best” to “which model uses the fewest resources,” with users stressing that efficiency metrics will drive hiring and tooling decisions.
4
retention and real‑world metrics
instead of isolated bench scores.
5
prompting quality trumps model swapping
.
6
Plenty of replies predict the chart will be turned into marketing collateral and...
Plenty of replies predict the chart will be turned into marketing collateral and call for clearer naming, methodology transparency, and better communication.
7
Tone ranges from skeptical to hostile
mocking, profanity, and blunt disbelief pepper the thread, underscoring frustration with the presentation.
8
Concrete asks include fixing factual errors, improving rate limits, and publishi...
Concrete asks include fixing factual errors, improving rate limits, and publishing clearer methodology or updated Composer results to restore credibility.
Top Reactions
Most popular replies, ranked by engagement
C
@cursor_ai
Learn more: https://t.co/AYcO0IQ33e
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@labomen001
Here's the graph with the same data, but plotted against the actual output cost for each (Composer 1.5 output from Cursor docs is $17.5). Although this doesn't account for >200K Opus 4.6/>272K GPT 5.4/Gemini 3.1 >200K.
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@carboxydev
damn i did not expect sonnet 4.5 to be so behind
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@dimitrioskonst
This graph loses credibility when codex and got5.4 are above Opus
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@browntechdude
Wait codex is better than Claude?
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