Mistral Medium 2505 vs Mistral Medium 2508
Mistral Medium 2505 (Mistral AI, 131,072-token context) versus Mistral Medium 2508 (Mistral AI, 131,072-token context). Mistral Medium 2505 is cheaper by 0% on a blended token mix. Mistral Medium 2508 uniquely supports vision input. Across 1 public benchmark we tracked, Mistral Medium 2505 wins 0 and Mistral Medium 2508 wins 1. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Mistral Medium 2505 vs Mistral Medium 2508
Mistral Medium 2505 and Mistral Medium 2508 are priced within 0% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
On capability surface area, the models diverge: Mistral Medium 2508 supports vision input where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: mistral-medium-2508
provider: mistral
fallback:
model: mistral-medium-2505
provider: mistral
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Mistral Medium 2505 | Mistral Medium 2508 | |
|---|---|---|
| Input price | $0.400/M | $0.400/M |
| Output price | $2.00/M | $2.00/M |
| Context window | 131,072 | 131,072 |
| Max output | 8,191 | 131,072 |
| Function calling | ✓ | ✓ |
| Vision | — | ✓ |
| Audio input | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
| Structured output | ✓ | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Mistral Medium 2505 | Mistral Medium 2508 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $240 /mo | $240 /mo | — |
| Mid-market 100K requests/day | $2,400 /mo | $2,400 /mo | — |
| Enterprise 1M requests/day | $24,000 /mo | $24,000 /mo | — |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your inputs include screenshots, diagrams, or product photos — Mistral Medium 2508 accepts image input natively, the other doesn't.
On arena-elo, Mistral Medium 2508 scores 22.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Mistral Medium 2505, switching to Mistral Medium 2508 means re-architecting that path (and vice versa).
- • Vision input
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Structured output (JSON schema)
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | Mistral Medium 2505 | Mistral Medium 2508 | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1387.0 | 1409.0 | Mistral Medium 2508 | +22.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 8,191 on Mistral Medium 2505 vs 131,072 on Mistral Medium 2508. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Mistral Medium 2508 has capabilities Mistral Medium 2505 lacks: Vision input. Worth wiring through the agent design before commit.
How to A/B test Mistral Medium 2505 vs Mistral Medium 2508 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Mistral Medium 2505 primary, mirror 20% of traffic to Mistral Medium 2508 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Mistral Medium 2505 vs Mistral Medium 2508
What is the context window of Mistral Medium 2505 versus Mistral Medium 2508? ▾
Mistral Medium 2505 supports up to 131,072 tokens of context. Mistral Medium 2508 supports up to 131,072 tokens. Mistral Medium 2508 has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Mistral Medium 2505 and Mistral Medium 2508 both support tool calling? ▾
Yes — both Mistral Medium 2505 and Mistral Medium 2508 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Mistral Medium 2505 and Mistral Medium 2508 process images? ▾
Mistral Medium 2508 accepts native image input. Mistral Medium 2505 does not — you would need to route image-heavy workloads through Mistral Medium 2508 or add a separate vision model in front of Mistral Medium 2505.
When should I choose Mistral Medium 2505 over Mistral Medium 2508? ▾
On the data this page surfaces, Mistral Medium 2505 is the right pick when Mistral Medium 2508's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
When should I choose Mistral Medium 2508 over Mistral Medium 2505? ▾
Your inputs include screenshots, diagrams, or product photos — Mistral Medium 2508 accepts image input natively, the other doesn't. On arena-elo, Mistral Medium 2508 scores 22.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test Mistral Medium 2505 against Mistral Medium 2508 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.