DeepSeek V4 Flash 0731 vs Ember-1: which should you choose in 2026?
DeepSeek V4 Flash 0731 (by DeepSeek) and Ember-1 (by Fireworks) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
DeepSeek V4 Flash 0731 is about 18.3× cheaper on a blended 3:1 input/output basis ($0.3275 vs $6.00 per 1M tokens).
Choose DeepSeek V4 Flash 0731 if…
- • lower blended API cost matters for your workload
Choose Ember-1 if…
- • you need image inputs
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | DeepSeek V4 Flash 0731 | Ember-1 |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | 90.8% Artificial Analysis | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | 38.6% Artificial Analysis | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 74.2% LiveBench | — |
Benchmark sources
- GPQA Diamond Benchmark Leaderboard
- Humanity's Last Exam Benchmark Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09.
Pricing, capabilities, and model facts
| Feature | DeepSeek V4 Flash 0731 | Ember-1 |
|---|---|---|
| Context & model facts | ||
| Developer | DeepSeek | Fireworks |
| API provider | DeepSeek | Fireworks |
| Input context | 1,048,576 tokens | 1,048,576 tokens |
| Maximum output | 943,718 tokens | 943,718 tokens |
| Released | — | — |
| Added to Writingmate | Jul 31, 2026 | Sep 24, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text | Text, Image |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | No | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.01 | $3.00 |
| Output (per 1M tokens) | $1.28 | $15.00 |
| Blended 3:1 input/output | $0.3275 | $6.00 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Sep 24, 2026.
Why Pay for Multiple Subscriptions?
Comparing DeepSeek V4 Flash 0731 from DeepSeek with Ember-1 from Fireworks? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | DeepSeek V4 Flash 0731 | Ember-1 | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
DeepSeek V4 Flash 0731 vs Ember-1 FAQ
Which is better, DeepSeek V4 Flash 0731 or Ember-1?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. DeepSeek V4 Flash 0731 is about 18.3× cheaper on a blended 3:1 input/output basis ($0.3275 vs $6.00 per 1M tokens).
Which model is cheaper to use through an API?
DeepSeek V4 Flash 0731 is about 18.3× cheaper on a blended 3:1 input/output basis ($0.3275 vs $6.00 per 1M tokens).
Which model supports more context?
The published context windows are close enough that this page does not treat the difference as a material advantage.
Can I switch between DeepSeek V4 Flash 0731 and Ember-1?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
Related comparisons
More DeepSeek V4 Flash 0731 comparisons
GPT-6.1 Sol Pro
OpenAI
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.
GPT-6.1 Sol
OpenAI
GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series.
Claude Sonnet 5.5
Anthropic
Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade.
Perceptron Mk1.5
Perceptron
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents.
GLM 5.3 Prime
Z.AI
GLM-5.3-Prime is the high-speed variant of Z.ai's GLM-5.3, inheriting its full capabilities while delivering 1.5–2× the output throughput through inference acceleration.
Qwen3.8 Max Prime
Qwen
Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point.
More Ember-1 comparisons
GPT-6.1 Sol Pro
OpenAI
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.
GPT-6.1 Sol
OpenAI
GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series.
Claude Sonnet 5.5
Anthropic
Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade.
Perceptron Mk1.5
Perceptron
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents.
Qwen3.8 Max Prime
Qwen
Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point.
Space Bunny Alpha
Stealth
Space Bunny Alpha is an anonymous large model with blazing-fast inference, strong coding capabilities and native multimodal input support.