Dola Seed 2.0 Lite vs Gemini 4 Argon
Which one, when
Dola-Seed-2.0-lite is the cheaper of the two: $0.25 input and $2 output per million tokens against $2 and $10 for gemini-4-argon, 8x less on input and 5x less on output, with cache reads at $0.05 against $0.1. Both take text, image, video and audio in, and Dola-Seed-2.0-lite allows twice the output (131072 against 65536) and lets you turn thinking off. Pick gemini-4-argon when one prompt must hold more than 256000 tokens, since its context is 1048576.
Benchmarks
Gemini 4 Argon: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| Dola Seed 2.0 Lite | Gemini 4 Argon | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.25 | $2 | 0.13× |
| Output / 1M tokens | $2 | $10 | 0.2× |
| Cache read / 1M tokens | $0.05 | $0.1 | 0.5× |
Rates from the live catalogue at build time; each model page carries the current rate card.
Where they sit · input price per 1M tokens across all 79 chat models on this billing unit (log scale)
Capabilities
| Dola Seed 2.0 Lite | Gemini 4 Argon | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | configurable | yes (vendor dial not published) |
| Structured output | yes | yes |
| Prompt caching | implicit + explicit | implicit + explicit |
| Cache lifetime | not published | not published |
| Minimum cached prefix | 1024 tokens | 4096 tokens |
Specs
| Dola Seed 2.0 Lite | Gemini 4 Argon | |
|---|---|---|
| Input modalities | text image audio video | text image audio video |
| Output modalities | text | text |
| Released | 2026-02-14 | 2026-10-13 |
| Knowledge cutoff | - | 2026-03 |
| Context window | 256K | 1M |
| Max output | 131K | 66K |
| Thinking parameter |
| - |
| Accepted values | thinking.type
reasoning_effort
| - |
| Default | enabled, with reasoning_effort medium | - |
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 Lite · Gemini 4 Argon
One prompt, both models, measured through the gateway
Dola Seed 2.0 Lite passed · 3 sentences
Enabling reasoning on our production LLM requires the model to run resource-intensive multi-step chain-of-thought inference for every customer-facing query, rather than the single-pass inference that powered our original non-reasoning deployment, and GPU compute—which makes up 90% of our total LLM bill—scales directly with the number of compute cycles each query consumes. Our cloud provider’s LLM billing model charges per million processed tokens, and reasoning workflows generate an average of 2.1x more hidden processing tokens (the intermediate problem-solving steps the model never surfaces to end users) that are still billable, pushing our total monthly token consumption just over double what it was prior to enabling the feature. […]
out 1306 tok (+1096 thinking) latency 18.2 s
Gemini 4 Argon passed · 3 sentences
Enabling the reasoning feature forces the AI to generate an extensive, hidden "chain of thought" to solve complex problems before delivering the final answer. Because LLM providers bill us per token generated, we are now paying for thousands of these invisible "thinking" words on top of the standard visible output. While this doubles our raw usage costs per query, it represents an investment in significantly higher accuracy and drastically reduced error rates for our most critical tasks.
out 91 tok latency 4.7 s
Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Dola Seed 2.0 Lite passed · 8/8 cases
### Corrected Function ```python def longest_run(xs): if not xs: # Handle empty input edge case return 0 best = cur = 1 # Base run length of 1 for any non-empty single element for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 # Reset run length for the new, different element return best ``` ### Bug Explanation (single paragraph) The original function’s core flaw was that it counted the number of consecutive equal adjacent element pairs, rather than the number of elements in the run itself, leading to all results being off by one, which caused both reported issues: a run of k equal elements only has k-1 consecutive matching pairs, so the original function’s maximum `best` value was always one less than the true element count of the longest run. […]
out 2620 tok (+2217 thinking) latency 33.2 s
Gemini 4 Argon 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 bug occurs because the original function counts the number of matching adjacent *pairs* rather than the actual number of *elements* in a run, causing all non-empty results to be off by one. Because a single element constitutes a valid run of length 1, initializing and resetting `cur` and `best` to `0` instead of `1` means the first element of any sequence is never counted; this causes single-element lists (where the loop doesn't run) to wrongly return `0` and makes it seem like runs at the end are missed because a pair of matching elements only increments `cur` to `1` instead of `2`. […]
out 286 tok latency 7.5 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Dola Seed 2.0 Lite passed · 5/5 fields, guidance "Formal guidance withheld pending Q4 clos"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Formal guidance withheld pending Q4 close" }
out 1502 tok (+1439 thinking) latency 20.2 s
Gemini 4 Argon passed · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```
out 68 tok latency 5.8 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Dola Seed 2.0 Lite missed · 141 words, 0 banned, 1 question
Today we launch Cross-Provider Prompt Cache, a new feature for our Core API Gateway built to cut redundant AI costs and latency for teams routing requests across multiple LLM providers. Many development teams rotate between OpenAI, Anthropic, and Google Gemini to balance performance, cost, and capability, but identical prompts get reprocessed from scratch with every provider switch, wasting tokens and slowing response times. This feature stores validated prompt responses at the gateway layer, so repeat requests pull from cache regardless of which provider they route to, with configurable TTLs and built-in compliance with all major provider data policies. […]
out 1870 tok (+1695 thinking) latency 23.1 s
Gemini 4 Argon passed · 120 words, 0 banned, 1 question
Are you paying twice for the exact same AI responses? Today, we are introducing Cross-Provider Prompt Caching for our API gateway, a new feature designed to reduce your artificial intelligence infrastructure costs and latency. When your applications send identical prompts to different large language models, our gateway now intercepts the request and serves a verified, cached response in milliseconds. It works across OpenAI, Anthropic, and open-source models simultaneously. If your primary provider experiences an outage, your fallback model still benefits from the shared cache Layer, preventing expensive redundant computations. Engineering teams can configure custom time-to-live settings, set similarity thresholds, and monitor cache hit rates directly from the dashboard. Activate Cross-Provider Prompt Caching in your gateway settings today and start saving.
out 156 tok latency 6.3 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-lite",
# model="gemini-4-argon", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
)
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-lite",
// model: "gemini-4-argon", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
});
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-lite",
# "model": "gemini-4-argon", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}]
}'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-lite",
// Model: "gemini-4-argon", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
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-lite")
// .model("gemini-4-argon") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, Dola Seed 2.0 Lite or Gemini 4 Argon?
Dola Seed 2.0 Lite is cheaper on the "Input / 1M tokens" row ($0.25 vs $2, 8.0× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.
Can I A/B test Dola Seed 2.0 Lite against Gemini 4 Argon without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.
Do Dola Seed 2.0 Lite and Gemini 4 Argon support prompt caching?
Yes. Both bill cache 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.