Dola Seed 2.0 Pro vs DeepSeek V4 Pro
Which one, when — curated verdict, not a benchmark table
Pick Dola-Seed-2.0-pro when your inputs include images or video, since it accepts text, image and video while deepseek-v4-pro is text-only, and its $0.5 input rate is about 3.2x cheaper than deepseek-v4-pro's $1.608 per million. Pick deepseek-v4-pro for very long single jobs: a 1000000-token context and 393216-token max output versus 256000 and 131072, with cache reads at $0.0134 that are roughly 7.5x cheaper than $0.1 when you replay a large prompt. Output prices are close ($3 vs $3.216), and both do chat, code, reasoning and tools with thinking that can be turned off.
Pricing
| Dola Seed 2.0 Pro | DeepSeek V4 Pro | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.5 | $1.608 | 0.31× |
| Output / 1M tokens | $3 | $3.216 | 0.93× |
| Cache read / 1M tokens | $0.1 | $0.0134 | 7.5× |
| Cache write | — | 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
| Dola Seed 2.0 Pro | DeepSeek V4 Pro | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | — | yes |
| Prompt caching | implicit + explicit | implicit (automatic) |
| Cache lifetime | not published | no fixed TTL (evicted when unused) |
| Minimum cached prefix | 1024 tokens | not published |
Specs
| Dola Seed 2.0 Pro | DeepSeek V4 Pro | |
|---|---|---|
| Input modalities | text image video | text |
| Output modalities | text | text |
| Released | 2026-02-14 | 2026-04-24 |
| Context window | 256K | 1M |
| Max output | 131K | 393K |
| Thinking parameter |
|
|
| Accepted values | thinking.type
reasoning_effort
| thinking.type
reasoning_effort
|
| Default | enabled, with reasoning_effort medium and thinking summary on | enabled, with reasoning_effort high some complex agent requests are automatically set to max |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Dola Seed 2.0 Pro · DeepSeek V4 Pro
One prompt, both models — measured through the gateway
Dola Seed 2.0 Pro passed · 3 sentences
Reasoning-enabled LLMs do not generate final answers in one pass: they run hundreds of unshown internal trial, cross-check, and correction steps for every user request, and every one of these hidden steps consumes billable GPU time just like the visible output text. Unlike our prior standard chat workload which only processed exactly what users typed, reasoning mode automatically executes these validation loops by default, with no visible indicator to end users that extra compute is being consumed. This 2x increase is not a billing error or rogue usage spike: industry benchmarks consistently put reasoning workloads at 1.7-2.6x the compute cost of equivalent standard LLM requests, and this pricing dynamic is almost never clearly disclosed on vendor rate cards.
out 563 tok (+416 thinking) latency 9.4 s
DeepSeek V4 Pro passed · 3 sentences
Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.
out 641 tok (+545 thinking) latency 11.5 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Dola Seed 2.0 Pro passed · 8/8 cases
### Corrected Implementation ```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 ``` --- ### Bug Explanation The original code has three critical linked flaws: 1. It initialized run counters `best` and `cur` to `0` instead of `1`, failing to account for the fact that *every individual element is already a valid run of length 1* -- this causes single-element inputs to incorrectly return `0`. 2. `cur` only counted how many extra matching elements followed the start of a run, so it always tracked one less than the actual run length, and it reset to `0` (instead of `1`) when a new run began, preserving this off-by-one error for all runs. 3. […]
out 1278 tok (+951 thinking) latency 16.7 s
DeepSeek V4 Pro 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 ``` **Explanation:** The bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]
out 2418 tok (+2131 thinking) latency 35.6 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Dola Seed 2.0 Pro passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 506 tok (+452 thinking) latency 8.6 s
DeepSeek V4 Pro passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }
out 1204 tok (+1153 thinking) latency 19.9 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Dola Seed 2.0 Pro passed · 123 words, 0 banned, 1 question
Today we’re rolling out cross-provider prompt caching for our API gateway. For engineering teams routing LLM requests across OpenAI, Anthropic, Mistral and open source models, this feature stores identical prompt payloads at the gateway layer, rather than relying on per-provider cache implementations limited to single endpoints. How much time and compute could your team save by avoiding redundant token processing for repeated system prompts, context windows, or common user queries? Cache hits return responses in under 10ms, with configurable TTL, granular purge controls, and per-application cache partitioning. Early access teams running support bots, batch inference and internal assistants recorded 42-67% lower LLM spend. This feature is live for all gateway users today, with no required code changes to existing routing workflows. (120 words)
out 1041 tok (+872 thinking) latency 11.4 s
DeepSeek V4 Pro no answer to grade · no answer text within 16,384 tokens (all of it went to thinking)
The model returned no answer text — the whole token budget went to hidden thinking.
out 8193 tok (+8192 thinking) latency 106.2 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="Dola-Seed-2.0-pro",
# model="deepseek-v4-pro", # 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: "Dola-Seed-2.0-pro",
// model: "deepseek-v4-pro", // 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": "Dola-Seed-2.0-pro",
# "model": "deepseek-v4-pro", # 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: "Dola-Seed-2.0-pro",
// Model: "deepseek-v4-pro", // 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("Dola-Seed-2.0-pro")
// .model("deepseek-v4-pro") // 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, Dola Seed 2.0 Pro or DeepSeek V4 Pro?
Dola Seed 2.0 Pro is cheaper on input / 1m tokens ($0.5 vs $1.608, 3.2× 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 Dola Seed 2.0 Pro against DeepSeek V4 Pro 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 Dola Seed 2.0 Pro and DeepSeek V4 Pro 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.