DeepSeek V4 Flash (0731) vs MiniMax M3
Which one, when — curated verdict, not a benchmark table
Both deepseek-v4-flash-0731 and minimax-m3 are text-out models with a 1000000-token context and near-identical input pricing at $0.308 and $0.3 per million, so the split is on output and reuse: minimax-m3 charges $1.2 per million output tokens, about 1.3x the $0.924 of deepseek-v4-flash-0731, and its $0.06 cache reads are roughly 1.9x the $0.0308 rate. Pick deepseek-v4-flash-0731 for high-volume text chat, code and reasoning where output and cache-heavy loops dominate the bill. Pick minimax-m3 when you need image or video input, a 524288-token max output, or the option to disable thinking.
Pricing
| DeepSeek V4 Flash (0731) | MiniMax M3 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.308 | $0.3 | 1× |
| Output / 1M tokens | $0.924 | $1.2 | 0.77× |
| Cache read / 1M tokens | $0.0308 | $0.06 | 0.51× |
| Cache write | no separate charge | no separate charge | — |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)
Capabilities
| DeepSeek V4 Flash (0731) | MiniMax M3 | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | yes — vendor dial not published | configurable |
| Structured output | yes | — |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | no fixed TTL (evicted when unused) | not published |
| Minimum cached prefix | not published | 512 tokens |
Specs
| DeepSeek V4 Flash (0731) | MiniMax M3 | |
|---|---|---|
| Input modalities | text | text image video |
| Output modalities | text | text |
| Released | 2026-07-31 | 2026-06-01 |
| Context window | 1M | 1M |
| Max output | 393K | 524K |
| Thinking parameter | — |
|
| Accepted values | — | thinking.type
reasoning_split
|
| Default | — | adaptive: thinking on, with the model deciding when extra reasoning helps |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: DeepSeek V4 Flash (0731) · MiniMax M3
One prompt, both models — measured through the gateway
DeepSeek V4 Flash (0731) passed · 3 sentences
Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.
out 365 tok (+266 thinking) latency 5.1 s
MiniMax M3 passed · 3 sentences
Reasoning-enabled models deliberately produce extensive internal "chain-of-thought" tokens before answering, often consuming 5–10x more tokens per query than a standard model. Because LLM providers bill per token for both that reasoning trace and the final response, your cost-per-query rises proportionally—and that multiplier is the primary driver of the doubled bill. The right response isn't to disable reasoning, but to route only genuinely complex queries to it while keeping routine traffic on the cheaper standard tier, which typically recovers 60–80% of the cost without sacrificing quality where it matters.
out 665 tok (+548 thinking) latency 5.7 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
DeepSeek V4 Flash (0731) passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` The original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]
out 1268 tok (+1016 thinking) latency 14.0 s
MiniMax M3 passed · 8/8 cases
The bug is in the loop bound: `range(1, len(xs))` iterates from index 1 up to but not including `len(xs) - 1`... wait, actually it goes up to `len(xs) - 1`, so the last index is included. The real problems are two: 1. **Starting `best` and `cur` at 0** and iterating from index 1 means a single-element list (`len(xs) == 1`) never enters the loop, so the function returns `0` instead of `1`. The fix is to either seed `best = cur = 1` when `xs` is non-empty, or include index 0 in the loop logic. 2. **Missing the final run**: the function only updates `best` *inside* the loop when it finds a continuation. If the longest run ends at the very last element, the loop body never gets to compare it after the increment — actually it does, but only if the run is longer than the previous `best`. […]
out 464 tok latency 7.9 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
DeepSeek V4 Flash (0731) passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }
out 1119 tok (+1072 thinking) latency 12.3 s
MiniMax M3 passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```
out 2212 tok (+2161 thinking) latency 23.3 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
DeepSeek V4 Flash (0731) missed · 141 words, 0 banned, 1 question
**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]
out 254 tok (+80 thinking) latency 4.4 s
MiniMax M3 passed · 120 words, 0 banned, 1 question
Introducing PromptCache, our new API gateway feature that caches prompts across multiple LLM providers. Developers can store prompt-completion pairs centrally, reducing redundant inference calls and lowering costs without sacrificing quality. How will your workflow change when repeated requests resolve instantly from a shared cache layer? The gateway intercepts outgoing requests, checks for matching prompt fingerprints, and serves previously generated completions when available, falling back to the original provider on misses. Compatible with OpenAI, Anthropic, Cohere, and custom endpoints, PromptCache integrates with existing routing rules and supports TTL, versioning, and per-tenant namespaces. Teams now gain predictable latency, reduced token spend, and improved throughput during traffic spikes today. […]
out 3256 tok (+2892 thinking) latency 21.5 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="deepseek-v4-flash-0731",
# model="minimax-m3", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "deepseek-v4-flash-0731",
// model: "minimax-m3", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-flash-0731",
# "model": "minimax-m3", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "deepseek-v4-flash-0731",
// Model: "minimax-m3", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("deepseek-v4-flash-0731")
// .model("minimax-m3") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, DeepSeek V4 Flash (0731) or MiniMax M3?
MiniMax M3 is cheaper on input / 1m tokens ($0.3 vs $0.308, 1.0× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.
Can I A/B test DeepSeek V4 Flash (0731) against MiniMax M3 without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do DeepSeek V4 Flash (0731) and MiniMax M3 support prompt caching?
Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.