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DeepSeek V4 Flash: The Free Alternative to GPT-4o — Speed & Price Test

DeepSeek V4 Flash should only be called a free GPT-4o alternative if a current free tier applies to your account and it passes the same real tasks at an acceptable latency and error rate. This guide provides a reproducible test for end-to-end latency, token usage, estimated cost, and output quality without inventing benchmark numbers.

Publication note: “Free alternative” is the hypothesis tested by this article, not a verified conclusion. Before publishing, confirm the current model ID, free-tier terms, quotas, and prices, run the script below, and replace every [RUN REQUIRED] field with measured data.

Why this comparison needs a real protocol

“Faster” and “cheaper” are incomplete claims. Results change with prompt length, output length, region, provider route, load, retries, and the type of work being tested. A useful comparison must answer four separate questions:

  1. Does each model complete the task correctly?
  2. How long does the full response take?
  3. How many input and output tokens does it consume?
  4. What does one accepted result cost?

The benchmark below tests three workload types:

  • structured extraction;
  • code repair;
  • constrained reasoning.

It deliberately does not use one trivia prompt as a proxy for production quality.

The benchmark contract

Setting Value
Models DeepSeek V4 Flash and GPT-4o
Runs per prompt 5 by default
Prompt order Randomized on every round
Temperature 0
Latency Client-observed end-to-end time
Speed Output tokens divided by end-to-end seconds
Cost Input and output tokens × verified per-token prices
Quality Human scoring against task-specific criteria

End-to-end latency is not time to first token. Measuring time to first token requires a streaming test and should be reported separately.

Setup

Install the client:

python -m pip install --upgrade openai
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Copy the exact current model IDs and prices from the provider pages available to your account. Prices below are intentionally required environment variables so stale numbers cannot silently enter the article.

export COMETAPI_API_KEY="your_api_key_here"
export DEEPSEEK_MODEL_ID="current_deepseek_v4_flash_model_id"
export GPT4O_MODEL_ID="current_gpt_4o_model_id"

export DEEPSEEK_INPUT_USD_PER_M="verified_input_price"
export DEEPSEEK_OUTPUT_USD_PER_M="verified_output_price"
export GPT4O_INPUT_USD_PER_M="verified_input_price"
export GPT4O_OUTPUT_USD_PER_M="verified_output_price"
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If a genuine free tier applies, keep the public list price in the cost calculation and report the free-tier allowance separately. This makes the result useful to readers who do not share the same promotion or account status.

Complete benchmark script

Save this as benchmark.py:

import json
import math
import os
import random
import statistics
import time
from pathlib import Path

from openai import OpenAI


def required(name: str) -> str:
    value = os.environ.get(name)
    if not value:
        raise RuntimeError(f"Set {name} before running the benchmark.")
    return value


def required_float(name: str) -> float:
    raw = required(name)
    try:
        return float(raw)
    except ValueError as exc:
        raise RuntimeError(f"{name} must be a number, got {raw!r}.") from exc


def percentile(values: list[float], fraction: float) -> float:
    ordered = sorted(values)
    index = max(0, math.ceil(fraction * len(ordered)) - 1)
    return ordered[index]


api_key = required("COMETAPI_API_KEY")
runs = int(os.environ.get("BENCHMARK_RUNS", "5"))
if runs < 3:
    raise RuntimeError("Use at least 3 runs per prompt; 5 or more is better.")

models = [
    {
        "label": "DeepSeek V4 Flash",
        "id": required("DEEPSEEK_MODEL_ID"),
        "input_usd_per_m": required_float("DEEPSEEK_INPUT_USD_PER_M"),
        "output_usd_per_m": required_float("DEEPSEEK_OUTPUT_USD_PER_M"),
    },
    {
        "label": "GPT-4o",
        "id": required("GPT4O_MODEL_ID"),
        "input_usd_per_m": required_float("GPT4O_INPUT_USD_PER_M"),
        "output_usd_per_m": required_float("GPT4O_OUTPUT_USD_PER_M"),
    },
]

tasks = [
    {
        "id": "extract",
        "prompt": (
            "Return JSON only with keys customer, plan, renewal_date, and risk. "
            "Input: Northwind Labs is on Pro. Renewal is 2026-09-30. "
            "The buyer says two failed exports may prevent renewal."
        ),
        "quality_check": (
            "Valid JSON; exact customer, plan, and date; risk mentions failed exports."
        ),
    },
    {
        "id": "code_repair",
        "prompt": (
            "Fix this Python function so a mutable default is not shared. Return the "
            "corrected function and a two-sentence explanation.\n\n"
            "def add_item(item, bucket=[]):\n"
            "    bucket.append(item)\n"
            "    return bucket"
        ),
        "quality_check": (
            "Uses None as the default, creates a list inside, and explains shared state."
        ),
    },
    {
        "id": "reasoning",
        "prompt": (
            "A batch job handles 120 records per minute. After every 600 records it "
            "pauses for exactly 30 seconds. Starting with no pause, how long does it "
            "take to finish 1,800 records? Show the assumptions and answer in minutes."
        ),
        "quality_check": (
            "States whether a pause after the final batch counts and calculates consistently."
        ),
    },
]

client = OpenAI(
    api_key=api_key,
    base_url="https://api.cometapi.com/v1",
    timeout=120.0,
    max_retries=0,  # Count failures instead of hiding them behind client retries.
)

results = []

for round_number in range(1, runs + 1):
    work = [(model, task) for task in tasks for model in models]
    random.shuffle(work)

    for model, task in work:
        started = time.perf_counter()
        row = {
            "round": round_number,
            "model": model["label"],
            "model_id": model["id"],
            "task": task["id"],
            "quality_check": task["quality_check"],
        }

        try:
            response = client.chat.completions.create(
                model=model["id"],
                messages=[{"role": "user", "content": task["prompt"]}],
                temperature=0,
            )
            elapsed = time.perf_counter() - started
            usage = response.usage
            input_tokens = usage.prompt_tokens if usage else 0
            output_tokens = usage.completion_tokens if usage else 0
            estimated_cost = (
                input_tokens * model["input_usd_per_m"] / 1_000_000
                + output_tokens * model["output_usd_per_m"] / 1_000_000
            )

            row.update(
                {
                    "ok": True,
                    "elapsed_seconds": elapsed,
                    "input_tokens": input_tokens,
                    "output_tokens": output_tokens,
                    "output_tokens_per_second": (
                        output_tokens / elapsed if elapsed else None
                    ),
                    "estimated_list_cost_usd": estimated_cost,
                    "output": response.choices[0].message.content or "",
                }
            )
        except Exception as exc:
            row.update(
                {
                    "ok": False,
                    "elapsed_seconds": time.perf_counter() - started,
                    "error_type": type(exc).__name__,
                }
            )

        results.append(row)
        print(
            f"round={round_number} model={model['label']} "
            f"task={task['id']} ok={row['ok']} "
            f"seconds={row['elapsed_seconds']:.2f}"
        )

Path("benchmark_results.json").write_text(
    json.dumps(results, ensure_ascii=False, indent=2),
    encoding="utf-8",
)

print("\nSummary (successful requests only)")
print("| Model | Success | Median latency | P95 latency | Median output tok/s | Total list cost |")
print("|---|---:|---:|---:|---:|---:|")

for model in models:
    rows = [row for row in results if row["model"] == model["label"]]
    successful = [row for row in rows if row["ok"]]
    if not successful:
        print(f"| {model['label']} | 0/{len(rows)} | n/a | n/a | n/a | n/a |")
        continue

    latencies = [row["elapsed_seconds"] for row in successful]
    speeds = [row["output_tokens_per_second"] for row in successful]
    total_cost = sum(row["estimated_list_cost_usd"] for row in successful)
    print(
        f"| {model['label']} | {len(successful)}/{len(rows)} "
        f"| {statistics.median(latencies):.2f}s "
        f"| {percentile(latencies, 0.95):.2f}s "
        f"| {statistics.median(speeds):.1f} "
        f"| ${total_cost:.6f} |"
    )

print("\nRaw responses are in benchmark_results.json. Score them blind.")
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Run it from a quiet test machine and record the region and timestamp:

python benchmark.py
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For stronger evidence, use at least 20 runs per prompt:

BENCHMARK_RUNS=20 python benchmark.py
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How to score quality

Remove the model names from benchmark_results.json before review. Give each response a binary pass/fail score against the quality_check field, then calculate:

quality pass rate = accepted responses / successful responses
cost per accepted result = total measured cost / accepted responses
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Cost per accepted result is usually more useful than price per million tokens. A cheap response that must be retried or rewritten may be the more expensive result.

Results

Replace this table after running the test. Do not publish the placeholders.

Metric DeepSeek V4 Flash GPT-4o
Successful requests [RUN REQUIRED] [RUN REQUIRED]
Quality pass rate [RUN REQUIRED] [RUN REQUIRED]
Median end-to-end latency [RUN REQUIRED] [RUN REQUIRED]
P95 end-to-end latency [RUN REQUIRED] [RUN REQUIRED]
Median output tokens/second [RUN REQUIRED] [RUN REQUIRED]
Verified input price / 1M tokens [VERIFY] [VERIFY]
Verified output price / 1M tokens [VERIFY] [VERIFY]
Cost per accepted result [RUN REQUIRED] [RUN REQUIRED]
Free-tier allowance and expiry [VERIFY] [VERIFY]

Also publish the test timestamp, client region, model IDs, number of runs, prompts, and failures. Without that context, readers cannot reproduce the comparison.

When “free” is a useful label

A free tier can be meaningful for prototypes, learning, or low-volume internal tools. It is not the same as a permanent zero-cost production service. Check:

  • whether the offer is a trial, recurring allowance, or temporary promotion;
  • request and token quotas;
  • rate limits and concurrency;
  • eligible accounts and regions;
  • overage behavior;
  • whether input and output tokens are treated differently;
  • the date the terms were last verified.

If any of these are unclear, change the headline before publication to “DeepSeek V4 Flash vs GPT-4o: A Reproducible Speed & Price Test.”

Verdict template

Use this only after the run:

In this test, DeepSeek V4 Flash achieved [X]% accepted outputs versus GPT-4o's [Y]%. Its median latency was [A]s and cost per accepted result was $[B], measured on [date] from [region]. The current free tier [did/did not] cover this workload. These results apply to the three published tasks, not to every use case.

Key takeaways

  • Treat “free,” “faster,” and “cheaper” as claims that require dated evidence.
  • Randomize run order and report median, P95, and failures.
  • Compare cost per accepted result, not price alone.
  • Publish the prompts, exact model IDs, prices, account conditions, and test timestamp.
  • Re-run the benchmark when providers change routes, models, or pricing.

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