Head to head: DeepSeek-V4-Flash vs cohere-command-a

DeepSeek-V4-Flash takes this one decisively: 105.0 to 90.5, with a 97% confidence win, plus an 8–2 edge in task victories. That’s not a vibes-based verdict; it’s a broad performance lead across coding, structured output, reasoning, and instruction-following. Cohere-command-a had moments, but this matchup was defined by DeepSeek being more reliable where reliability actually matters.

The clearest pattern is that DeepSeek-V4-Flash was simply the sharper executor. It won on the Python log redactor by using a stricter IPv4 regex and cleaner replacement order; on the SQL task by producing the more robust, portable query; on the concurrency bug fix by giving the correct in-flight promise pattern without contradictory detours; and on the CSV-to-JSON task by doing the underrated hard part: following the output contract exactly. The JavaScript dedupe fix was a tie because both models solved it cleanly, but across the code-heavy set DeepSeek looked like the model less likely to create new problems while fixing the old one.

Cohere-command-a’s wins came in places where line editing and business writing mattered more than strict constraint discipline. It was better on Precise proofreading, catching grammar errors DeepSeek missed, and it won the vendor-delay email by including the required revised delivery date and covering the brief more completely. Those are real wins, not consolation prizes. But they also underline the larger issue: outside those pockets, Cohere too often diluted good underlying work with avoidable instruction-following mistakes—extra commentary, markdown fences around JSON, unsupported additions in a faithful rewrite, or messy intermediate reasoning where the prompt asked for just the answer.

That’s why the margin feels earned rather than accidental. Even in closer tasks, DeepSeek usually had the cleaner edge: clearer arithmetic in warehouse-pick logic, more natural localization for a Spanish mobile toast, and tighter contradiction analysis. There were two ties, including strict JSON extraction, but even there the split reflected the same theme—Cohere sometimes preserved nuance better, yet still tripped over formatting requirements. In production-style evaluations, that tradeoff usually loses.

Final call: DeepSeek-V4-Flash is the better all-around model here, and comfortably so. Cohere-command-a is respectable on selective editing and customer-facing prose, but DeepSeek was more dependable, more exact, and far less likely to fumble the brief.

How they were tested

We ran 12 fresh text tasks, generated on the fly for this matchup so neither model could prepare in advance, and had gpt-5.4 score each one. To cancel position bias, every task was judged twice — once in each presentation order — and every number reported here, including the headline totals, is the average of both passes. DeepSeek-V4-Flash scored 105.0 to cohere-command-a's 90.5.

1. python-log-redactor

Practical coding — Write a Python 3 function redact_log(line: str) -> str that masks sensitive values in application logs. Replace any email address with [EMAIL] and any IPv4 address with [IP]. Keep all other text unchanged. If a token matches both patterns, treat it as an email first. Examples: - "login by sara.ivey@luma.co from 10.4.8.23" -> "login by [EMAIL] from [IP]" - "retry to 192.168.0.1 failed; owner=ops@acme.net" -> "retry to [IP] failed; owner=[EMAIL]" Return code only.

Winner: DeepSeek-V4-Flash — Model A is better because it correctly replaces emails first and uses a stricter IPv4 regex that avoids matching invalid addresses like 999.999.999.999. Model B generally follows the task, but its IPv4 pattern is overly permissive, reducing correctness. (Second judge pass, order swapped — scores are the average of both: Model A is better because it correctly prioritizes email replacement and uses a stricter IPv4 regex that avoids matching invalid addresses like 999.999.999.999. Model B generally works, but its IP pattern is overly permissive and its email regex is slightly sloppier.)

2. Precise proofreading

Correct ONLY the grammar, spelling, and punctuation errors in the sentence below without changing wording, tone, or meaning, then on a second line list each change as 'was -> now'. Sentence: "Its been a long time since we seen there new office, and me and him was suprised how quite it was."

Winner: cohere-command-a — Model B is better because it fixes more of the actual grammar errors in the sentence, including "seen" to "saw," "me and him" to "he and I," and "was" to "were." Model A follows the format more closely but leaves major grammatical errors uncorrected, while Model B’s main weakness is changing the output format and adding unnecessary commentary. (Second judge pass, order swapped — scores are the average of both: Model B corrects all grammar, spelling, and punctuation errors and provides a change list, though it adds unnecessary extra commentary and quotation marks. Model A misses several grammar corrections ('seen' to 'saw' and 'me and him was' to 'he and I were') and also fails to apply its listed 'Its -> It's' correction in the corrected sentence.)

3. vendor-delay-email

Professional writing — Rewrite this internal note as a customer-facing email. Audience: procurement manager at Northglass Clinics. Tone: calm, accountable, no blame. Length: 120–150 words. Must include: revised delivery date, what shipped already, what remains, one concrete mitigation, and a clear call to action. Source note: "Northglass PO 18477 got split because our Brno warehouse shorted 6 of the MedRail mounting kits after the pallet count was wrong. We already sent 24 blood-draw chairs on June 3 via ArrowFreight, tracking AF493118. The missing 6 kits and 12 instrument trays will leave June 11 after the supplier cross-dock lands on the 10th. If they need partial install, field ops can send Luis Ortega for a half-day next Thursday, but only if they confirm by Tuesday 2 p.m. Central."

Winner: cohere-command-a — Model B better satisfies the brief by explicitly including a revised delivery date while maintaining a calm, accountable customer-facing tone and covering all required status details. Model A is clear and professional, but it omits the revised delivery date and its mitigation is framed more as an optional service than a concrete delay-reduction step. (Second judge pass, order swapped — scores are the average of both: Model B better satisfies the brief by explicitly including a revised delivery date, what has shipped, what remains, a mitigation step, and a clear call to action in a calm, accountable tone. Model A is polished, but it omits the required revised delivery date and is slightly less complete on the mandated elements.)

4. Faithful rewrite

Rewrite this blunt internal note as a warm, professional Slack message to the whole engineering team (60–90 words), keeping every fact intact and adding no new commitments. Return only the message. Note: "Deploy is frozen until the memory leak in the image service is fixed. Priya is on it. Do not merge to main. ETA tomorrow noon."

Winner: DeepSeek-V4-Flash — Model A is warmer and professional while preserving the core facts with only minor softening of the ETA. Model B adds unsupported claims and commitments like "making great progress," "we’ll keep you updated," and extra motivational language, which violates the instruction to add no new commitments. (Second judge pass, order swapped — scores are the average of both: Model A is more faithful to the original note while still sounding warm and professional. Model B adds unsupported claims and commitments like "making great progress," "we’ll keep you updated," and motivational language, which violates the instruction to keep every fact intact and add no new commitments.)

5. warehouse-pick-logic

Step-by-step reasoning — A warehouse packs clinic starter bundles. Each bundle needs 2 sensor strips, 1 charger, and 3 sterile caps. Current stock: 95 sensor strips, 34 chargers, 140 sterile caps. There are already 7 complete bundles prepacked and set aside; their parts are not included in the stock counts above. Today, an urgent order consumes 5 chargers and 18 sterile caps from the current stock. Then a restock arrives with 21 sensor strips and 26 sterile caps. After that, the team repacks all possible complete bundles using only the remaining current stock. How many complete bundles can ship by end of day in total, including the 7 prepacked bundles?

Winner: DeepSeek-V4-Flash — Model A and Model B both reach the correct total of 36 and follow the requested step-by-step approach. Model A is slightly better because it is a bit clearer and more precise, explicitly noting the whole-number cap calculation and presenting the stock transitions more cleanly. (Second judge pass, order swapped — scores are the average of both: Model B and Model A both reach the correct total of 36 and follow the requested step-by-step approach. Model A is slightly better because it is a bit clearer and more precise in handling the sterile-cap division by explicitly noting 49 complete bundles with remainder 1, whereas Model B uses an approximate decimal.)

6. javascript-dedupe-fix

Debugging & code review — The following JavaScript function is supposed to return a new array of users, keeping only the first occurrence of each email address case-insensitively. It should preserve original order and not mutate the input. It currently fails on mixed-case duplicates. javascript function uniqueUsers(users) { const seen = new Set(); return users.filter(u => { if (seen.has(u.email)) return false; seen.add(u.email.toLowerCase()); return true; }); } Fix the bug and return the corrected code only.

Winner: Tie — Model A and Model B both correctly normalize the email before checking and adding it to the Set, which fixes the mixed-case duplicate bug while preserving order and not mutating the input. Both also follow the instruction to return corrected code only, and the only difference is a variable name that does not affect quality. (Second judge pass, order swapped — scores are the average of both: Model B and Model A both correctly fix the bug by normalizing the email to lowercase before both checking and adding it to the Set, preserving order and avoiding input mutation. Both also follow the instruction to return corrected code only, and the differences are purely variable naming.)

7. sql-late-invoices

SQL & data queries — Given this schema: clients(client_id INT, client_name TEXT, tier TEXT) invoices(invoice_id INT, client_id INT, issued_date DATE, due_date DATE, paid_date DATE, amount DECIMAL(10,2)) Write a single SQL query to return, for each client in tier 'gold', these columns: client_name, invoice_count, late_count, late_rate. Count an invoice as late only when paid_date > due_date. Include gold clients with zero invoices. late_rate should be late_count / invoice_count rounded to 3 decimals, and be 0 when invoice_count is 0. Sort by late_rate descending, then client_name ascending.

Winner: DeepSeek-V4-Flash — Model A gives a correct single-query solution that includes gold clients with zero invoices, computes late_count and late_rate properly, and sorts as requested. Model B is mostly correct, but it relies on SELECT aliases in ORDER BY and uses a PostgreSQL-specific cast, making it a bit less portable and slightly less clean for the prompt. (Second judge pass, order swapped — scores are the average of both: Model A is better because it provides a simpler single-query solution that correctly includes gold clients with zero invoices, computes late invoices only when paid_date > due_date, handles divide-by-zero safely, and sorts as requested. Model B is mostly correct, but its late_rate expression does not coalesce late_count inside the calculation, so clients with zero invoices can yield NULL instead of 0 depending on SQL behavior.)

8. Concurrency bug fix

This TypeScript function is meant to memoize an async loader but has a race: concurrent callers can each trigger the underlying fetch. Fix it so the fetch runs at most once per key, and a rejected fetch does NOT poison the cache (a later call must retry). Return ONLY the corrected function. ts const cache = new Map<string, Promise<string>>(); async function load(key: string, fetcher: (k: string) => Promise<string>) { if (cache.has(key)) return cache.get(key)!; const value = await fetcher(key); cache.set(key, Promise.resolve(value)); return value; }

Winner: DeepSeek-V4-Flash — Model A gives a concise, correct fix: it stores the in-flight promise before awaiting, so concurrent callers share one fetch, and it deletes the cache entry on rejection so retries work. Model B eventually includes a correct version, but it violates the instruction to return only the corrected function and contains multiple incorrect or contradictory intermediate attempts, including use of a nonexistent Promise.status property and versions that would poison the cache with errors. (Second judge pass, order swapped — scores are the average of both: Model A gives a concise, correct fix: it stores the in-flight promise before awaiting, ensuring only one fetch per key, and deletes the cache entry on rejection so retries work. Model B eventually includes a correct version, but it violates the instruction to return only the corrected function and contains multiple contradictory and incorrect intermediate attempts.)

9. messy-csv-to-json

Data wrangling / structured output — Convert the messy records below into valid JSON as an array of objects with exactly these keys in this order: id (integer), full_name (string), department (string), start_date (YYYY-MM-DD), remote (boolean), salary_usd (number). Rules: trim spaces, title-case names, department must be one of "Finance", "Operations", "Product", "Support", convert dates to ISO, parse salary as a number, and sort output by id ascending. Records: "104 | mila chen | product | 4/9/2022 | yes | $98,500" "101|DARIO HOLT|Finance|2021-11-03|No|112000" "103 | avery singh | support | 09-17-2023 | TRUE | USD 67,250 " "102| noor al-salem | operations | 2020/02/01 | false | 84500.00"

Winner: DeepSeek-V4-Flash — Model A fully satisfies the prompt with valid JSON output only, correct normalization, and proper sorting. Model B's data content is equally correct, but it violates the instruction to output valid JSON as the response by adding explanatory text and markdown outside the JSON array. (Second judge pass, order swapped — scores are the average of both: Model A is better because it provides exactly the requested valid JSON array and all transformed values appear correct. Model B’s JSON content is also correct, but it adds explanatory text and markdown fencing, so it does not strictly satisfy the instruction to respond with just the structured output.)

10. Strict JSON extraction

Extract every meeting from this text into a JSON array of {"title","day","start24","durationMinutes"}. Use 24-hour "HH:MM" for start24, integer minutes for duration, and title-case titles. Return ONLY valid JSON, no commentary. Text: "Standup is daily at 9am for a quarter hour. The design review runs thursday from 2:30-4pm. Payroll sync — first monday, 11:00 to noon."

Winner: Tie — Model A is better because it returns valid JSON with no extra formatting and correctly extracts the times and durations. Model B preserves the more specific day phrase "First Monday," but it violates the instruction to return only valid JSON by wrapping the array in a code fence. (Second judge pass, order swapped — scores are the average of both: Model B is better because it correctly preserves the recurrence detail in "First Monday" and normalizes day values more cleanly, while Model A loses that specificity by reducing it to "monday." Both extract the meetings and times correctly, but Model B has a minor instruction-following issue from including Markdown code fences around the JSON.)

11. Localization with tone

Translate this app onboarding line into natural, friendly European Spanish suitable for a mobile toast (keep it under 60 characters, no exclamation marks): "You're all set — your first backup starts tonight." Return only the translation, then the character count in parentheses.

Winner: DeepSeek-V4-Flash — Model A is more natural and concise for a mobile toast, and it stays comfortably under the 60-character limit while preserving the friendly tone. Model B is understandable but less idiomatic in this context, more verbose, and its reported character count appears inaccurate. (Second judge pass, order swapped — scores are the average of both: Model A is better because it stays comfortably under 60 characters and sounds natural and friendly for a mobile toast in European Spanish. Model B is understandable, but its stated character count appears inaccurate and the phrasing is a bit heavier for this UI context.)

12. Find the contradiction

The following spec contains exactly one internal contradiction. Quote the two conflicting sentences verbatim and explain the conflict in one sentence. Do not fix it. Spec: "Free accounts may create up to three projects. Every account, regardless of tier, may archive unlimited projects. Archiving a project does not count against the project limit. Free accounts are limited to three projects total, including archived ones."

Winner: DeepSeek-V4-Flash — Model A is better because it identifies the same conflicting pair while giving a slightly more precise explanation that explicitly ties the contradiction to the sentence stating archived projects do not count against the limit. Model B is mostly correct, but its explanation adds the unsupported idea of "active projects," making it a bit less exact. (Second judge pass, order swapped — scores are the average of both: Model B and Model A both identify the same two quoted sentences, but neither selects the actual contradictory pair because the conflict is really between "Archiving a project does not count against the project limit." and "Free accounts are limited to three projects total, including archived ones." Both follow the format reasonably well and explain the issue clearly, so they are effectively equal.)

See every prompt and the full side-by-side outputs in the interactive Head-to-Head.