DeepSeek V4 Flash Vision Exp vs GPT-5.3 CodexWhich Is Better in 2026?
DeepSeek V4 Flash Vision Exp vs GPT-5.3 Codex: which should you choose in 2026?
DeepSeek V4 Flash Vision Exp (by DeepSeek) and GPT-5.3 Codex (by OpenAI) 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.
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
DeepSeek V4 Flash Vision Exp has the larger context window (1,048,576 tokens vs 400,000 tokens).
Choose DeepSeek V4 Flash Vision Exp if…
- • you work with longer documents, transcripts, or codebases
Choose GPT-5.3 Codex if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
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 Vision Exp | GPT-5.3 Codex |
|---|---|---|
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 | — | 77.3% OpenAI report |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | 74.0% OpenAI report |
A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols. Resolved | — | 56.8% OpenAI report |
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 | — | — |
The individual-contributor Diamond subset of SWE-Lancer, which evaluates economically valuable real-world software-engineering tasks. Tasks completed | — | 81.4% OpenAI report |
Version 2.0 of the benchmark for completing realistic tasks in terminal environments. Results must not be merged with Terminal-Bench 2.1. Pass rate | — | 77.3% OpenAI report |
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 | — | 79.1% Terminal-Bench |
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 | — | 92.6% OpenAI report |
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 | — | — |
Cybersecurity CTFs Capture-the-flag challenges used to evaluate an agent's practical cybersecurity task performance. Challenges completed | — | 77.6% OpenAI report |
| Arena preference scores | ||
Human preference score for code and web development | — | Ambiguous / conflicting rows |
Benchmark sources
- Arena AI leaderboard
- Introducing GPT-5.4
- Introducing GPT-5.3-Codex
- Terminal-Bench 2.1 Verified Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 25, 2026.
Pricing, capabilities, and model facts
| Feature | DeepSeek V4 Flash Vision Exp | GPT-5.3 Codex |
|---|---|---|
| Context & model facts | ||
| Developer | DeepSeek | OpenAI |
| API provider | DeepSeek | OpenAI |
| Input context | 1,048,576 tokens | 400,000 tokens |
| Maximum output | 384,000 tokens | 128,000 tokens |
| Released | — | Feb 5, 2026 |
| Added to Writingmate | Aug 21, 2026 | Feb 24, 2026 |
| License | Not available | Proprietary |
| Knowledge cutoff | — | Not disclosed |
| Capabilities | ||
| Inputs | Text, Image | Text, Image, File |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Base input (per 1M tokens) | $0.44 | $1.75 |
| Base output (per 1M tokens) | $1.32 | $14.00 |
| Higher-context pricing tiers | Base rate only | Base rate only |
| Price-comparison caveat | At least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like. | |
| 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
- Introducing GPT-5.3-Codex
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Aug 21, 2026.
Why Pay for Multiple Subscriptions?
Comparing DeepSeek V4 Flash Vision Exp from DeepSeek with GPT-5.3 Codex from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | DeepSeek V4 Flash Vision Exp | GPT-5.3 Codex | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
DeepSeek V4 Flash Vision Exp vs GPT-5.3 Codex FAQ
Which is better, DeepSeek V4 Flash Vision Exp or GPT-5.3 Codex?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. DeepSeek V4 Flash Vision Exp has the larger context window (1,048,576 tokens vs 400,000 tokens).
Which model is cheaper to use through an API?
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
Which model supports more context?
DeepSeek V4 Flash Vision Exp has the larger context window (1,048,576 tokens vs 400,000 tokens).
Can I switch between DeepSeek V4 Flash Vision Exp and GPT-5.3 Codex?
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.
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