Tested prompt · Writing
Rewrite corporate jargon in plain words: every AI model's reply, tested
We sent this everyday writing prompt to all 20 models in llmwise, the same way the app sends a message, and checked every reply the same way. Here's each one as it came, with whether it passed, what it cost and how long it took.
Based on 20 of our test runs on , through OpenRouter with the app's own prompt and settings. Updated .
Short answer
13 of 20 models passed this writing prompt's check (rubric); Claude Haiku 5.5, GPT-6.1 Sol, GPT-6 Sol, Gemini 3.8 Flash, DeepSeek V4.1 Flash, Grok 4.7, and GLM 5.3 Flash didn't. The cheapest reply that passed was GPT-6 Luna's, at $0.00010; the fastest, Claude Haiku 4.5's in 1.6 s. The dearest reply, GPT-6 Astra's, cost 140 times as much ($0.0145).
The prompt, as sent, and its check
Checked by rubric (graded), the same way for every model.
Rewrite corporate jargon in plain words (everyday)
Rewrite this for a general audience in at most 90 words, keeping every fact:
"Leveraging our cross-functional synergies, the Q3 initiative operationalized a customer-centric paradigm shift, yielding a 12% uplift in retention KPIs and a 30-basis-point reduction in churn velocity across the enterprise segment, while our omnichannel touchpoints were right-sized to optimize bandwidth."
Graded on:
- Plain words: Plain words a general reader understands; no jargon left.
- Keeps the facts: Keeps the facts: a Q3 project, 12% better retention, churn down 0.3 percentage points among large business customers, fewer or better-sized support channels.
- Clear: Short and clear.
Exactly what this prompt's replies are checked against, with every other prompt of our test runs.
Every model's result
All 20 models on this prompt, in catalog order.
| Model | Result | Cost | Time | Reply |
|---|---|---|---|---|
| Claude Fable 5.1Anthropic | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.0130 | 6.0 s | 114 tokens |
| Claude Opus 5.5Anthropic | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.0078 | 5.1 s | 112 tokens |
| Claude Sonnet 5.5Anthropic | Passed: Graded 4.7 of 5 on average (lowest 4). | $0.0025 | 1.6 s | 102 tokens |
| Claude Sonnet 5Anthropic | Passed: Graded 4.7 of 5 on average (lowest 4). | $0.0022 | 2.7 s | 105 tokens |
| Claude Haiku 5.5Anthropic | Failed: Graded 3.3 of 5 on average (lowest 2). | $0.00012 | 1.3 s | 95 tokens |
| Claude Haiku 4.5Anthropic | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.00083 | 1.6 s | 73 tokens |
| GPT-6 AstraOpenAI | Passed: Graded 4.0 of 5 on average (lowest 4). | $0.0145 | 6.0 s | 72 tokens |
| GPT-6.1 SolOpenAI | Failed: Graded 3.7 of 5 on average (lowest 3). | $0.0013 | 3.3 s | 72 tokens |
| GPT-6 SolOpenAI | Failed: Graded 3.7 of 5 on average (lowest 3). | $0.0033 | 5.0 s | 77 tokens |
| GPT-6 LunaOpenAI | Passed: Graded 4.7 of 5 on average (lowest 4). | $0.00010 | 2.4 s | 65 tokens |
| Gemini 4 ArgonGoogle | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.0011 | 4.1 s | 78 tokens |
| Gemini 3.1 Pro (preview)Google | Passed: Graded 4.0 of 5 on average (lowest 3). | $0.0101 | 8.8 s | 76 tokens |
| Gemini 3.8 FlashGoogle | Failed: Graded 3.7 of 5 on average (lowest 3). | $0.00064 | 3.1 s | 81 tokens |
| DeepSeek V4.1 FlashDeepSeek | Failed: Graded 3.7 of 5 on average (lowest 3). | $0.00030 | 0.9 s | 87 tokens |
| DeepSeek V4 ProDeepSeek | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.0024 | 5.8 s | 58 tokens |
| Grok 4.7xAI | Failed: Graded 3.7 of 5 on average (lowest 3). | $0.0112 | 20.0 s | 60 tokens |
| Kimi K3Moonshot | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.0030 | 2.0 s | 82 tokens |
| GLM 5.3Z.ai | Passed: Graded 4.0 of 5 on average (lowest 3). | $0.00020 | 1.8 s | 57 tokens |
| GLM 5.3 FlashZ.ai | Failed: Graded 3.3 of 5 on average (lowest 3). | $0.000097 | 4.9 s | 73 tokens |
| Mistral Large 4Mistral | Passed: Graded 4.3 of 5 on average (lowest 4). | $0.0030 | 34.3 s | 0 tokens |
Cost: what OpenRouter charged us for the reply. Time: from sending to the whole reply. Reply: its length in tokens, thinking not counted. In llmwise you pay per message, not per token: each of these replies counted as one message on Pro.
Every reply
The 7 replies that failed first, then the ones that passed, from the cheapest up.
GLM 5.3 Flash
Failed: Graded 3.3 of 5 on average (lowest 3).
In Q3, teams from across the company worked together on an initiative that put customers first. The result: customer retention improved by 12%, and the rate at which enterprise customers left fell by 30 basis points. We also adjusted our customer contact channels—like phone, email, and chat—so each team could focus on what it does best.
435 tokens in, 124 out (51 of them reasoning) · 4.9 s · $0.0001 · 1 message on Pro · answered by z-ai/glm-5.3-flash via AtlasCloud ·
Claude Haiku 5.5
Failed: Graded 3.3 of 5 on average (lowest 2).
Through teamwork across departments, our Q3 initiative shifted our approach to focus more on customers. Customer retention rose 12%, and customer loss slowed by 30 basis points across our enterprise segment. We also adjusted our multichannel customer contact points to use resources more efficiently.
600 tokens in, 95 out · 1.3 s · $0.0001 · 1 message on Pro · answered by anthropic/claude-haiku-5.5 via Anthropic ·
DeepSeek V4.1 Flash
Failed: Graded 3.7 of 5 on average (lowest 3).
In the third quarter, teams from across the company worked together on a project to make the customer the main focus. The results: retention measures rose 12%, and the rate at which customers left fell by 30 basis points (0.30 percentage points) in the enterprise segment. We also adjusted the size of our channels — online, in-store, phone, and others — so we could use our resources more efficiently.
459 tokens in, 195 out (108 of them reasoning) · 0.9 s · $0.0003 · 1 message on Pro · answered by deepseek/deepseek-v4.1-flash via Together ·
Gemini 3.8 Flash
Failed: Graded 3.7 of 5 on average (lowest 3).
By having different teams work together in the third quarter, we refocused our business on customer needs. This effort paid off: client retention rates increased by 12%, and the rate at which enterprise clients canceled dropped by 0.30% (30 basis points). At the same time, we streamlined and balanced our communication channels to make better use of our team’s time and resources.
447 tokens in, 81 out · 3.1 s · $0.0006 · 1 message on Pro · answered by google/gemini-3.8-flash via Google ·
GPT-6.1 Sol
Failed: Graded 3.7 of 5 on average (lowest 3).
In the third quarter, teams across departments worked together to put customers first. The initiative increased customer retention by 12% and reduced the rate at which customers leave by 0.3 percentage points (30 basis points) in the enterprise segment. We also adjusted customer contact points across all channels to make better use of available capacity.
419 tokens in, 185 out (113 of them reasoning) · 3.3 s · $0.0013 · 1 message on Pro · answered by openai/gpt-6.1-sol via OpenAI ·
GPT-6 Sol
Failed: Graded 3.7 of 5 on average (lowest 3).
In Q3, teams across the company worked together to put a more customer-focused approach into practice. In the enterprise customer segment, retention measures improved by 12%, while the rate at which customers left fell by 30 basis points (0.3 percentage points). We also adjusted how we connect with customers across channels to make better use of our capacity.
417 tokens in, 246 out (169 of them reasoning) · 5.0 s · $0.0033 · 1 message on Pro · answered by openai/gpt-6-sol via OpenAI ·
Grok 4.7
Failed: Graded 3.7 of 5 on average (lowest 3).
In the third quarter, teams from different parts of the company worked together to put a customer-focused approach into practice. Retention measures rose 12%, and the rate of customer loss fell by 30 basis points among enterprise customers. Contact points across all channels were also resized to use capacity more efficiently.
1,658 tokens in, 1,603 out (1,543 of them reasoning) · 20.0 s · $0.0112 · 1 message on Pro · answered by x-ai/grok-4.7 via xAI ·
GPT-6 Luna
Passed: Graded 4.7 of 5 on average (lowest 4).
In Q3, teams across the company worked together to focus more on customers. Retention measures improved by 12%, and the rate of customer loss fell by 0.3 percentage points in the enterprise segment. The company also adjusted its customer contact channels to make better use of available capacity.
417 tokens in, 124 out (59 of them reasoning) · 2.4 s · $0.0001 · 1 message on Pro · answered by openai/gpt-6-luna via OpenAI ·
GLM 5.3
Passed: Graded 4.0 of 5 on average (lowest 3).
In Q3, our teams worked together to make the business more customer-focused. As a result, customer retention rose 12%, and churn in our enterprise segment fell by 0.3 percentage points. We also adjusted our customer service channels to use our resources more efficiently.
434 tokens in, 98 out (41 of them reasoning) · 1.8 s · $0.0002 · 1 message on Pro · answered by z-ai/glm-5.3 via Baidu ·
Claude Haiku 4.5
Passed: Graded 4.3 of 5 on average (lowest 4).
Here's a clearer version: Our Q3 initiative brought different departments together to focus on customer needs. The results were strong: we kept 12% more customers and reduced customer loss by 0.3 percentage points among large businesses. We also streamlined our customer contact channels to work more efficiently. (77 words)
462 tokens in, 73 out · 1.6 s · $0.0008 · 1 message on Pro · answered by anthropic/claude-haiku-4.5 via Amazon Bedrock ·
Gemini 4 Argon
Passed: Graded 4.3 of 5 on average (lowest 4).
In the third quarter, different teams worked together on a new project to put customers first. Among our large business clients, this effort improved customer retention metrics by 12% and slowed the rate of customer cancellations by 0.30 percentage points (30 basis points). At the same time, we adjusted all our customer communication channels to free up staff capacity.
668 tokens in, 78 out · 4.1 s · $0.0011 · 1 message on Pro · answered by google/gemini-4-argon via Google AI Studio ·
Claude Sonnet 5
Passed: Graded 4.7 of 5 on average (lowest 4).
In Q3, teams worked together to focus more on customers' needs. This effort increased customer retention by 12% and reduced the rate of customer loss (churn) by 0.3 percentage points among large business clients. The company also adjusted the number of customer contact channels (like phone, email, and chat) to better manage workload and resources.
597 tokens in, 105 out · 2.7 s · $0.0022 · 1 message on Pro · answered by anthropic/claude-sonnet-5 via Claude Platform on AWS ·
DeepSeek V4 Pro
Passed: Graded 4.3 of 5 on average (lowest 4).
In the third quarter, our teams worked together to focus more on customers. This produced a 12% increase in customer retention and a 0.30 percentage point drop in churn among business customers. We also fine-tuned our various customer contact channels to use resources more efficiently.
431 tokens in, 576 out (518 of them reasoning) · 5.8 s · $0.0024 · 1 message on Pro · answered by deepseek/deepseek-v4-pro-0813 via Wafer ·
Claude Sonnet 5.5
Passed: Graded 4.7 of 5 on average (lowest 4).
In Q3, teams from different departments worked together to put customers first. The result was a 12% increase in customer retention and a 0.30 percentage-point drop in the rate at which enterprise customers leave. We also reduced the number of ways we contact customers (such as email, phone, and chat) to free up staff time and resources.
601 tokens in, 102 out · 1.6 s · $0.0025 · 1 message on Pro · answered by anthropic/claude-sonnet-5.5 via Anthropic ·
Mistral Large 4
Passed: Graded 4.3 of 5 on average (lowest 4).
During Q3, our teams collaborated across departments to focus on customers. This shift improved customer retention by 12% and cut churn by 0.3 percentage points among business clients. We also fine-tuned our customer service channels across all platforms to use resources more efficiently.
427 tokens in, 1,273 out (1,369 of them reasoning) · 34.3 s · $0.0030 · 1 message on Pro · answered by mistralai/mistral-large-4-0 via Mistral ·
Kimi K3
Passed: Graded 4.3 of 5 on average (lowest 4).
In Q3, teams across the company worked together to focus more on customers. As a result, customer retention improved by 12%, and the rate at which enterprise customers left dropped by 30 basis points (0.3%). We also streamlined our communication channels—such as phone, email, and online—to use our time and resources more efficiently.
504 tokens in, 120 out (38 of them reasoning) · 2.0 s · $0.0030 · 1 message on Pro · answered by moonshotai/kimi-k3 via Together ·
Claude Opus 5.5
Passed: Graded 4.3 of 5 on average (lowest 4).
In the third quarter, teams from different departments worked together on a project to put customers at the center of how the company operates. As a result, customer retention measures rose 12%, and the rate at which large business customers left dropped by 0.3 percentage points. The company also streamlined its customer contact channels, including phone, email, and online, to make better use of staff time and resources.
599 tokens in, 245 out (133 of them reasoning) · 5.1 s · $0.0078 · 1 message on Pro · answered by anthropic/claude-opus-5.5 via Claude Platform on AWS ·
Gemini 3.1 Pro
Passed: Graded 4.0 of 5 on average (lowest 3).
In Q3, our teams worked together to adopt a more customer-focused approach. For our business clients, this resulted in a 12% increase in customer retention and a 0.30% (30 basis points) decrease in the rate of customers leaving. Additionally, we adjusted our various customer communication channels to make more efficient use of our time and resources.
447 tokens in, 763 out (687 of them reasoning) · 8.8 s · $0.0101 · 1 message on Pro · answered by google/gemini-3.1-pro-preview via Google ·
Claude Fable 5.1
Passed: Graded 4.3 of 5 on average (lowest 4).
In Q3, teams from different departments worked together on a project to put customers first. Among enterprise (large business) customers, this raised the retention rate by 12% and slowed the rate at which customers left by 0.3 percentage points. The company also cut back on some of the ways it communicates with customers—across channels like phone, email, and in-app—to free up staff time.
599 tokens in, 114 out · 6.0 s · $0.0130 · 1 message on Pro · answered by anthropic/claude-fable-5.1 via Anthropic ·
GPT-6 Astra
Passed: Graded 4.0 of 5 on average (lowest 4).
In Q3, teams across departments worked together to shift toward a more customer-focused approach. Among enterprise customers, the initiative increased retention by 12% and reduced the rate at which customers leave by 0.3 percentage points (30 basis points). We also adjusted customer interactions across all channels to make better use of available capacity.
417 tokens in, 206 out (134 of them reasoning) · 6.0 s · $0.0145 · 1 message on Pro · answered by openai/gpt-6-astra via OpenAI ·
More writing prompts
The other writing prompts, each with every model's reply, and the results across all five.
Questions
Which AI does best on “Rewrite corporate jargon in plain words”?
13 of 20 models passed this writing prompt's check (rubric); Claude Haiku 5.5, GPT-6.1 Sol, GPT-6 Sol, Gemini 3.8 Flash, DeepSeek V4.1 Flash, Grok 4.7, and GLM 5.3 Flash didn't. The cheapest reply that passed was GPT-6 Luna's, at $0.00010; the fastest, Claude Haiku 4.5's in 1.6 s. The dearest reply, GPT-6 Astra's, cost 140 times as much ($0.0145).
What does a reply to “Rewrite corporate jargon in plain words” cost?
Through the models' APIs, what OpenRouter charged us ran from $0.000097 (GLM 5.3 Flash) to $0.0145 (GPT-6 Astra) for this prompt. The cheapest that passed was GPT-6 Luna's, $0.00010. In llmwise you don't pay by the token: a reply like these counts as one message on Pro, whichever model answers.
Claude, GPT, Gemini, DeepSeek, Grok, Kimi, GLM, and Mistral, in one chat.
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